{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hide\n",
    "!pip install -Uqq fastbook\n",
    "import fastbook\n",
    "fastbook.setup_book()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hide\n",
    "from fastbook import *"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "[[chapter_multicat]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Other Computer Vision Problems"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the previous chapter you learned some important practical techniques for training models in practice. Considerations like selecting learning rates and the number of epochs are very important to getting good results.\n",
    "\n",
    "In this chapter we are going to look at two other types of computer vision problems: multi-label classification and regression. The first one is when you want to predict more than one label per image (or sometimes none at all), and the second is when your labels are one or several numbers—a quantity instead of a category.\n",
    "\n",
    "In the process will study more deeply the output activations, targets, and loss functions in deep learning models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multi-Label Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Multi-label classification refers to the problem of identifying the categories of objects in images that may not contain exactly one type of object. There may be more than one kind of object, or there may be no objects at all in the classes that you are looking for.\n",
    "\n",
    "For instance, this would have been a great approach for our bear classifier. One problem with the bear classifier that we rolled out in <<chapter_production>> was that if a user uploaded something that wasn't any kind of bear, the model would still say it was either a grizzly, black, or teddy bear—it had no ability to predict \"not a bear at all.\" In fact, after we have completed this chapter, it would be a great exercise for you to go back to your image classifier application, and try to retrain it using the multi-label technique, then test it by passing in an image that is not of any of your recognized classes.\n",
    "\n",
    "In practice, we have not seen many examples of people training multi-label classifiers for this purpose—but we very often see both users and developers complaining about this problem. It appears that this simple solution is not at all widely understood or appreciated! Because in practice it is probably more common to have some images with zero matches or more than one match, we should probably expect in practice that multi-label classifiers are more widely applicable than single-label classifiers.\n",
    "\n",
    "First, let's see what a multi-label dataset looks like, then we'll explain how to get it ready for our model. You'll see that the architecture of the model does not change from the last chapter; only the loss function does. Let's start with the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our example we are going to use the PASCAL dataset, which can have more than one kind of classified object per image.\n",
    "\n",
    "We begin by downloading and extracting the dataset as per usual:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.vision.all import *\n",
    "path = untar_data(URLs.PASCAL_2007)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset is different from the ones we have seen before, in that it is not structured by filename or folder but instead comes with a CSV (comma-separated values) file telling us what labels to use for each image. We can inspect the CSV file by reading it into a Pandas DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fname</th>\n",
       "      <th>labels</th>\n",
       "      <th>is_valid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000005.jpg</td>\n",
       "      <td>chair</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000007.jpg</td>\n",
       "      <td>car</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000009.jpg</td>\n",
       "      <td>horse person</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000012.jpg</td>\n",
       "      <td>car</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000016.jpg</td>\n",
       "      <td>bicycle</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        fname        labels  is_valid\n",
       "0  000005.jpg         chair      True\n",
       "1  000007.jpg           car      True\n",
       "2  000009.jpg  horse person      True\n",
       "3  000012.jpg           car     False\n",
       "4  000016.jpg       bicycle      True"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(path/'train.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the list of categories in each image is shown as a space-delimited string."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sidebar: Pandas and DataFrames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No, it’s not actually a panda! *Pandas* is a Python library that is used to manipulate and analyze tabular and time series data. The main class is `DataFrame`, which represents a table of rows and columns. You can get a DataFrame from a CSV file, a database table, Python dictionaries, and many other sources. In Jupyter, a DataFrame is output as a formatted table, as shown here.\n",
    "\n",
    "You can access rows and columns of a DataFrame with the `iloc` property, as if it were a matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       000005.jpg\n",
       "1       000007.jpg\n",
       "2       000009.jpg\n",
       "3       000012.jpg\n",
       "4       000016.jpg\n",
       "           ...    \n",
       "5006    009954.jpg\n",
       "5007    009955.jpg\n",
       "5008    009958.jpg\n",
       "5009    009959.jpg\n",
       "5010    009961.jpg\n",
       "Name: fname, Length: 5011, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fname       000005.jpg\n",
       "labels           chair\n",
       "is_valid          True\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0,:]\n",
    "# Trailing :s are always optional (in numpy, pytorch, pandas, etc.),\n",
    "#   so this is equivalent:\n",
    "df.iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also grab a column by name by indexing into a DataFrame directly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       000005.jpg\n",
       "1       000007.jpg\n",
       "2       000009.jpg\n",
       "3       000012.jpg\n",
       "4       000016.jpg\n",
       "           ...    \n",
       "5006    009954.jpg\n",
       "5007    009955.jpg\n",
       "5008    009958.jpg\n",
       "5009    009959.jpg\n",
       "5010    009961.jpg\n",
       "Name: fname, Length: 5011, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['fname']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can create new columns and do calculations using columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b\n",
       "0  1  3\n",
       "1  2  4"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_df = pd.DataFrame({'a':[1,2], 'b':[3,4]})\n",
    "tmp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b  c\n",
       "0  1  3  4\n",
       "1  2  4  6"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_df['c'] = tmp_df['a']+tmp_df['b']\n",
    "tmp_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas is a fast and flexible library, and an important part of every data scientist’s Python toolbox. Unfortunately, its API can be rather confusing and surprising, so it takes a while to get familiar with it. If you haven’t used Pandas before, we’d suggest going through a tutorial; we are particularly fond of the book [*Python for Data Analysis*](http://shop.oreilly.com/product/0636920023784.do) by Wes McKinney, the creator of Pandas (O'Reilly). It also covers other important libraries like `matplotlib` and `numpy`. We will try to briefly describe Pandas functionality we use as we come across it, but will not go into the level of detail of McKinney’s book."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### End sidebar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have seen what the data looks like, let's make it ready for model training."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Constructing a DataBlock"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How do we convert from a `DataFrame` object to a `DataLoaders` object? We generally suggest using the data block API for creating a `DataLoaders` object, where possible, since it provides a good mix of flexibility and simplicity. Here we will show you the steps that we take to use the data blocks API to construct a `DataLoaders` object in practice, using this dataset as an example.\n",
    "\n",
    "As we have seen, PyTorch and fastai have two main classes for representing and accessing a training set or validation set:\n",
    "\n",
    "- `Dataset`:: A collection that returns a tuple of your independent and dependent variable for a single item\n",
    "- `DataLoader`:: An iterator that provides a stream of mini-batches, where each mini-batch is a tuple of a batch of independent variables and a batch of dependent variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On top of these, fastai provides two classes for bringing your training and validation sets together:\n",
    "\n",
    "- `Datasets`:: An object that contains a training `Dataset` and a validation `Dataset`\n",
    "- `DataLoaders`:: An object that contains a training `DataLoader` and a validation `DataLoader`\n",
    "\n",
    "Since a `DataLoader` builds on top of a `Dataset` and adds additional functionality to it (collating multiple items into a mini-batch), it’s often easiest to start by creating and testing `Datasets`, and then look at `DataLoaders` after that’s working."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we create a `DataBlock`, we build up gradually, step by step, and use the notebook to check our data along the way. This is a great way to make sure that you maintain momentum as you are coding, and that you keep an eye out for any problems. It’s easy to debug, because you know that if a problem arises, it is in the line of code you just typed!\n",
    "\n",
    "Let’s start with the simplest case, which is a data block created with no parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "dblock = DataBlock()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can create a `Datasets` object from this. The only thing needed is a source—in this case, our DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "dsets = dblock.datasets(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This contains a `train` and a `valid` dataset, which we can index into:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4009, 1002)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dsets.train),len(dsets.valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(fname       008663.jpg\n",
       " labels      car person\n",
       " is_valid         False\n",
       " Name: 4346, dtype: object,\n",
       " fname       008663.jpg\n",
       " labels      car person\n",
       " is_valid         False\n",
       " Name: 4346, dtype: object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x,y = dsets.train[0]\n",
    "x,y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, this simply returns a row of the DataFrame, twice. This is because by default, the data block assumes we have two things: input and target. We are going to need to grab the appropriate fields from the DataFrame, which we can do by passing `get_x` and `get_y` functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'008663.jpg'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x['fname']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('005620.jpg', 'aeroplane')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dblock = DataBlock(get_x = lambda r: r['fname'], get_y = lambda r: r['labels'])\n",
    "dsets = dblock.datasets(df)\n",
    "dsets.train[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, rather than defining a function in the usual way, we are using Python’s `lambda` keyword. This is just a shortcut for defining and then referring to a function. The following more verbose approach is identical:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('002549.jpg', 'tvmonitor')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_x(r): return r['fname']\n",
    "def get_y(r): return r['labels']\n",
    "dblock = DataBlock(get_x = get_x, get_y = get_y)\n",
    "dsets = dblock.datasets(df)\n",
    "dsets.train[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lambda functions are great for quickly iterating, but they are not compatible with serialization, so we advise you to use the more verbose approach if you want to export your `Learner` after training (lambdas are fine if you are just experimenting)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the independent variable will need to be converted into a complete path, so that we can open it as an image, and the dependent variable will need to be split on the space character (which is the default for Python’s `split` function) so that it becomes a list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Path('/home/jhoward/.fastai/data/pascal_2007/train/002844.jpg'), ['train'])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_x(r): return path/'train'/r['fname']\n",
    "def get_y(r): return r['labels'].split(' ')\n",
    "dblock = DataBlock(get_x = get_x, get_y = get_y)\n",
    "dsets = dblock.datasets(df)\n",
    "dsets.train[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To actually open the image and do the conversion to tensors, we will need to use a set of transforms; block types will provide us with those. We can use the same block types that we have used previously, with one exception: the `ImageBlock` will work fine again, because we have a path that points to a valid image, but the `CategoryBlock` is not going to work. The problem is that block returns a single integer, but we need to be able to have multiple labels for each item. To solve this, we use a `MultiCategoryBlock`. This type of block expects to receive a list of strings, as we have in this case, so let’s test it out:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(PILImage mode=RGB size=500x375,\n",
       " TensorMultiCategory([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.]))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dblock = DataBlock(blocks=(ImageBlock, MultiCategoryBlock),\n",
    "                   get_x = get_x, get_y = get_y)\n",
    "dsets = dblock.datasets(df)\n",
    "dsets.train[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, our list of categories is not encoded in the same way that it was for the regular `CategoryBlock`. In that case, we had a single integer representing which category was present, based on its location in our vocab. In this case, however, we instead have a list of zeros, with a one in any position where that category is present. For example, if there is a one in the second and fourth positions, then that means that vocab items two and four are present in this image. This is known as *one-hot encoding*. The reason we can’t easily just use a list of category indices is that each list would be a different length, and PyTorch requires tensors, where everything has to be the same length."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> jargon: One-hot encoding: Using a vector of zeros, with a one in each location that is represented in the data, to encode a list of integers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s check what the categories represent for this example (we are using the convenient `torch.where` function, which tells us all of the indices where our condition is true or false):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(#1) ['dog']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idxs = torch.where(dsets.train[0][1]==1.)[0]\n",
    "dsets.train.vocab[idxs]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With NumPy arrays, PyTorch tensors, and fastai’s `L` class, we can index directly using a list or vector, which makes a lot of code (such as this example) much clearer and more concise.\n",
    "\n",
    "We have ignored the column `is_valid` up until now, which means that `DataBlock` has been using a random split by default. To explicitly choose the elements of our validation set, we need to write a function and pass it to `splitter` (or use one of fastai's predefined functions or classes). It will take the items (here our whole DataFrame) and must return two (or more) lists of integers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(PILImage mode=RGB size=500x333,\n",
       " TensorMultiCategory([0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def splitter(df):\n",
    "    train = df.index[~df['is_valid']].tolist()\n",
    "    valid = df.index[df['is_valid']].tolist()\n",
    "    return train,valid\n",
    "\n",
    "dblock = DataBlock(blocks=(ImageBlock, MultiCategoryBlock),\n",
    "                   splitter=splitter,\n",
    "                   get_x=get_x, \n",
    "                   get_y=get_y)\n",
    "\n",
    "dsets = dblock.datasets(df)\n",
    "dsets.train[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we have discussed, a `DataLoader` collates the items from a `Dataset` into a mini-batch. This is a tuple of tensors, where each tensor simply stacks the items from that location in the `Dataset` item. \n",
    "\n",
    "Now that we have confirmed that the individual items look okay, there's one more step we need to ensure we can create our `DataLoaders`, which is to ensure that every item is of the same size. To do this, we can use `RandomResizedCrop`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "dblock = DataBlock(blocks=(ImageBlock, MultiCategoryBlock),\n",
    "                   splitter=splitter,\n",
    "                   get_x=get_x, \n",
    "                   get_y=get_y,\n",
    "                   item_tfms = RandomResizedCrop(128, min_scale=0.35))\n",
    "dls = dblock.dataloaders(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now we can display a sample of our data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7gxWHMwXZbB+/vKBentE2S7w3TKp9yErauqZrLKZdopRnUuXUqwZjWkwDCFgMDs/DB4+ROO6MNWitsH5KZ9dcXpzw3je/wfrikkLnGGNZrRqaVYvrPEU1YbZ/g/3jm2R5gbGW1hi8sahoOGkVwFNZFuRVRl5qskxwtqNtBNoO0YLWghZB62BxOJ3ROoe7cZMbr38SuZ9zcfEY14Fp4PTsjDWebtVgTYdXnjIDXIdZN6h8QuFhUkzIvEJyT3V8hzc/+3le+74vMpvv0dY1SkGeK5wIN24dQ1YgbU17uWR9uGK6t0dVFmhdobUmUxqHwloPohGle8VS5BqlwHqP0x5VeEoNqAylZzStYrWuWa6WiAhlPiUrC7LnSP4Za9FeB+ULiHhwGwXrvRsAhqj8ZDM37pr/NoZUvKaXXtlYN1Bi8SYBTCiC8tmeowXBJ+XoRh01QCkysH7HNxAXzrfWxrZZhtPwVcbd+Pv4WTf7hMASpHk2MgZeBn21bfQmA3bTpoHuIQInF+aKse7ZMNUeER+s8/T7RaDmRXDIFkvSm/2+b9aALInGNOH3926zQqH34TuO+Nf35zkXnlcDIldHCjwVECRUdJVCHyvT1IHDztgl4+sMzx8ChV33HivgXdd9GlNxFWgZ3v+qdu4akOOBuOtfkrHiH7ojxmAiAYox2Njlmrh2U1wtKstwzuC8T4YV1jqsc7RdQ5ZlKK9wEWlnWqN0sJydNxjjwdqgWFRGVs5g7yZFfogqS9R8zmQyI59UgUo3DRihUBk2c9gsB4R2tebx/QcUs0Nu5AWzvWMypWiaGms68B7TWaxxgMI7Q1s3WGswzlAvl+RK0axXhCxWx/nZhzTrFYvzcxYnF+S6JJtNqMoD9MwzrYKSyMqSYjphsr9HWRbU6zWsa0RabALZOKoyR+eKYAg7vO2wxoVJJyso8pwiL8hzjRLBWY81hlYJ2WyKLXMa72lagydjdvMWy9Ul68VDpllBs2qwxuImBbmGTBcc3nyRzmvKIo/365gd7LP3ykvMjg+YlTPwDoUjyzTGeiaTguneEcuVI5+VIBrTtdgyh3yK0gqUIssKtBe8l2Af+TB5Kq3wzuG86xUfeLT2lBOFzgq6dk2RabzKyKsJ+V6JN91zGcNAZCntgKuOijxZ7yJhDG/NR+4JJjdJmqufxsYma317/9UM51DXp/O3dIBPzfdb5wRxER9sjL5A8z/JTo8/D78PjbYtQBCVfr/d2/7avrfBw/Ml5nj4HOnvEwalqB407GLS04N778G5COYiqxLbFYgBgcTKbKgMVGJgiI+AROIkKv84rUmiEFzUy14iI7rNUqSxfpU8FRBchSyHnT72ue+i6q+i5a9yQ+xSpmPFepXy3dWGcRvH7R1b/eNjx9uuYhl2MRbjvhha/7vasAsUDNmGYfzC8LmG38f99fczYFBKaNsOZ10ch2Es5nkOEHyI4nEm9Z3HWBOoOPE4a/HOIyiUzlC5QqucaVYFv32uyTJNspo6Z0BBnuUo51hjcbbDNA0nDx4ikxm6qiiKCpVpMicR0Tu6psM7hxZFY1vaZoV1DussOIvPNNbUXJ4vaes1i/NzmlVNoUpm5U0Ojm+RT6ZM5vuIKLx4VKbQRY51jkxDphS6M+jM4HEoHSYcY1qU9oDBWYXtwESa2IkiyxQ606hMI+k98RYI/aqqDKcUdedYrdYY7zk8PkatM1h1TPfmlEWJm2RMDvapypw817zw+vdgamjqFavlKXW7wOQZj9cL9hbnKMkI9pDDWodzBi+O6XyPpVtSzWcIHtvVeJvhXQ6isYCo8JsJwWp1NrgsHB7vwHoiWIiPowSVaQqBXClyneG1RmcKyXKsfX6MnXURELDtNggS5i47UExArwCHMlS4T3OVjuez8f6ta0YLd9e+nUaVDI6Lz+IJ9Pv2HBavPppLdzG9Q+P1yjb7BKIs3g0YZS+9C2EYf5b01q779dudx+F2gojNts3v1Lth4ifnI+Uv0j9r7+9xrgey8abhTB+v4NJvKL1LwUfWANm4eBJQE9ndP0N5KiDY5Tsfdsh42y5lu0vpjzt1KEMQMt6eto1dGbss6CRjyztdf6xQh9e5akDtatewg68KbtzVT7uR5JP33PUMw/iEtH8YrzDuh+Fz/X0HDrwNr4t3KAmBg0JOLhlZrqMbIbpz4oTfdZayrILPWEB0CBrUImjxyKxCSQ55HuIRXPD/G2fpuhadZ7jMYduOtr6gaxc419K1lscP7rN3tI/taqr9GZkqguHXGZQO/mCcY7W8YLG4oOsMWmfMZhVkOV3XsjhfcHm6wLee+ewmL957heN7L1Ee7tHaNrAcKk4SIuRFgekaunpFu1phnY2UsqCzDGctnTHYVUteZGgdqFElmqosowXqsdZQNxbVCVqpaIl7iizDiWa2v09WTlnWhsX6jG4y4XjvmKOjV5jNb1Du7TM7PuLg1jG5aDot3HzxZWTZ8ujd9zCd0LSey8UK8833mc1v4q2iEg+mBWfBeWpTo4oMJRZsi21afOnIpURsi3U5VuXgPYUKbcURPd4eYywmWdOiwkSpQkCZ7wy55JTVHq25wIjB2hXL9RLTPD9A4JxD/HA+9j3bnuaTbXY2HjaaXsbG11WgYBflPpzTr56//BOW/va9k4Xuo4IL56Tt43nS+21AoJTaGTO2q+1bbfCAODbYIAABkPiueHxvwW+MWKUUolSw0kfz9q4+Guu0zW8SfoxIDmz6wfkQR6AEcdv9Gnz9g36O/eYSLUDc3Y+DNO+He1rn8N4M2iRbv80ueSogWK1W/Q8w/oFTZw33jX+IXYhpOKDGoCHJLms7XWM4EMYRoEqprbiG8TlDK3wYLDjePnzO8ed0n10vx9OCG8eDafzSPU3Scw1lGJswBgtjd0OKVRgGPe7qz2+lTb9SxNmGSZlRFhrnIjASwViHtS3WGZy1OBOQep5n7M3nGOexKke8I9dZYPO8xTuDc4a2rclnU8oyQytwzuLaBmcacB2Pz9aYxYLz++9weXKftl6gfIZbLXn09jc5yTK8FnQ1oZrOqKZT5tWE5nKFx3F2fk5drxEPRe5ZLqHyJY/rmmm1z73XXmb/8Jjp/j5SZKhpBpkwz+bM53O0UlhraVuDtR2ZFLi2DuNVC6hgMSvRdMbRtCHLQangJxY8putweYZSwuryjCzLKYqCoshReUEuis51SJaRFyU3bt7kw9k+OquYzm6Q2ZIbL32SOy+8xPzgkKbrcM5Q5II2hunhPlIJ07Lg9cNPcvv8JR7ef8D9x+9TzEq0n7I8X+G0A9PiTBfGrAj5rCC7b7EXC9R0Qu4zSsnJRFHXHWoyIdd58Nk6G3WOAYQizzCmRlQWQI3EiHZRGOlQxjPbP6K1nnZ1QWc6tGTk5fPL0rbG4Lci2T0pah62jZHNnJws793G1a75dfg9bRvT4Wl72qeVHvLRQdEPmJeeEh989xB93L4HBWEOs/FSPrJ5m/sN7zts23j/LqNNCD9zIldCUKBEViI0ytqgTEUksEtKIWJ7gBVAsuqfUQ/m+zT3C0kXhfiAOOMjKoK0FKuw0eKR6g+gANnsE+/x8X59AGfquz6UcOPw8D6FfEa94IOhGLKgEnLYzdYneSogaNs2PvtuhXwVKBgq6uG+pKyvUrZj18IYhY7P26XYxvccU2RDhZ7n+db+oTIdKtwxUzKUcX/s6qvhdbz3fft20VC7nudp9xwyAVmWbV33KiCUvieQkP4lhmF4/K90VsGaFm8l9A1hwumMjZSdIssyOmswbR0QuSvIcxWChcTQWQHlwBpM02Ca4L/PNHRqwcP7D1HKYHzLYr3GtHB5/wN001LlE9ZtTWM7VJlzUMyZSMb68pSVM4hXWDROZWRFwXw6JfOCFBlOhFyXTLIJZTZBlOb4xl0QKGcV1WyCLnOkUOzv75HnGd46JmWBVj5YBniqQmOtp2lMiBSLAV86UzivMZ3FGIdzEulGTa5LyjIn04JpW3xT01mDmkxoTYtpMsqyxE8mIEKWCVUmPDo7Yb24QOmc27fu8MO/7b9BeeM2vlvTtIYcASnIck2J0DU1rTh0UZBlHdkB3Jwcs3d3n8lkwlTDer2iXl1Q5IpyNsVYR21rJJ9hspxMKzontE6QomRS7eGNAp3T1esApjMdJ0RPZzuwQl6UgALRm7huAck0iGGaQ57vMV9XXCwaLtcrrG2e2zgOQNYhJAU1fi8380VgQuNm2cxLffQ6PX/cHzOUXYbL2PBJn51zkFwVid5/4pohFFES3e0HXvtIf4d5ZmjBb55prOSH8/EY3KTjt55FBqR9MqJSP/jEGCcw4p9gCJISH87dCsEPDVDnYupyzGTain8ITIcX2U5XjEDDEm7hk0tKBBVZDRdjGgTpcWuCftHJEIizyMyk4M/AIKkNEGHjMniafCRAMLScdyns4Q+zC01edUzqzDGwGPpuxgpuFy2f/g4V2XiQ7Ar8GLYpDbrUjizbdM1wAI6V7K42Dds6pu6H546fY3jN8Tm7nheezIp44mUYbBv2cQJE43sOWYUEEoaplLv6Ylc7f9mIN6zWdV9jwKNQWc5kUtG1DZ2pEe0QZWjXK5qloV7lhKjtHC0TWuUwdoWxHQ6h8Q3N5Sne16wfPcB3NYjD4bGdhdqTi2cpBUr2qPQeUniWApfUdK5GNRbTWVAaL7C8sFyKcLR3k6qoUKWGas7k4BYv3nudo1s3OV8uMF2L1uBsh7cK5UskZkigPKu2plIVSuvenaFEUFrIMk1RFBhjcVawxtN1IXq6mpQ40yHiMbZDtR5iNL73wfeeCSG+whi8s3RdQ1aVTPM5F48f8NYvfImLswccv3DMa5/+LJP5HNO1LJYNIh7XBUVgsawzR54VTKWADpSEqHKNZ1IWiHV0ztKtLwGDcYB1OK+pu45pNaPa26OzLdY6Wu/xKOZ7+5iVZdUaTLdGlEIXBVpncVwHilY5Q56XgRWBMMs6TwAJAVw5b3Au9FNdL6i7i+c0iKFrHVnmCeoDGIagJ0s6mN29lamiIgzvezxUQrAZA+agTyZMRghDel7iNQKIEATvU8R9dMJIaEoPOvymjoBEdJCS43ogEZX/1tzuk+tgML/Gpw2KVgjp+xvKfGB0D643mCOFDdOwpSPC/bwL97AxxiSegigXmTIbWLPEEKgQW+CVipEtDnBopXDeYp2Kij4wbW5zwaCRB5kfiNqAHe+30gE3IDVa9xJcCvHpA2iJjJFzDudDpoNz4VmssyA+xtD43m1q7dPn6m+pDsFYWT0tLW6M6oaWef8Dy3YgxnDbVWzBUIbKfdffXcr0iXxS77fcDLvAzK5zdinhq5Rr6iet9c5gwqv676NsHzMJT/uxrzp23M8JCQ8BwzCgccgeDN0Uu2I4ntWm77bYtsO2LZ0jvBxZhu0M68Upy5MLdDWls0u0bVFdgzYdVZPhnWXhgCwnF8FbgxPBlRNM3bB6eMJ0opGVRVuFZIJRJlij8wzrHMiMsrjJfH7E/uEB+0d7XC4f8NWv/z2caNbdGYijyEvKrMCsLW61wtYr2kxT3dnj6PgmL7z8Yki3Kwtc25JpcN6GSdobmvUSYzRZUdKhsPUKhQeX6mFY8kxTziZU0ylZUSGXlzjrMd6Q5Rl5pjCZCvEEnQHnsE7hcWSxXkMbM2J8DMwyRjC2pWtb3v7K13jw4SN0XnHnpVd4+Y03yYqSTIdUSsRSToswSXqPFwe64LJtyXLNBIUCnDN0xqCzjLZtQByaNPEJ4hzHB3tc3jjgww8mLNq2j4EIwaOGKtc0DbRGIAPfGTpjcd6Tq7xn0gSHi7naWaY3aXs+Q2eqj7/IS4UsL4YM/ccunTF4rwdzkcf35YiG8yBxS/islcb1Fu7GegzHRoXp0/UGxkQKFLQyuF5UTH7obohWvyQlFo63xgXaXaKFHUmJXvlFxe0jY+7dcD4ctoTgw2d7zvJxntlMXbG9LgXsJYo+sCmxy0Biuql1ff0G74PbYKiccYKSaGVHRkMiwAoBhzHNM9N02PC+E8CHEumVtYrtQgWWqr+OCEgIGnQDWsTHDpH+N/V9Pw4NWuc9NrJFzoeR4JzDGodzoR6Bj+9aaL8LwM8/fT5+KiCoqmpr8h9bvbty58efx1TPLks2Kf9kfV6lsK5S2sPPCUwMZdiRu4BGyjHdBRjSNYey6xmeBhDG/Tf8PO6ToeU/BFzDNj9LwT6tXcN7DEHe8B4JdQ63J9B0VSDmrsJUz2JRPg5ZLpbgPcqF4ELRgncWdXmCvzhlvRCctYgzZM4yUZ5pmWG6mmXjaR0suxbrHCrPyWdTtFMcTqYszYLW5pRqguQOqxZY66l0xf7skHv3Ps3+0QtM5jPKKiNTgnu35c7xKxwf3uXBgw95fPohbbciy3OwFidCbVq80xwe3uT49k2KWYWIoiwmdKsVXbMKMRDehZoFpqPIY3CgF1rCMwbDROGMwWrNdD4nz0vyKZTW0LQ1utPB4ss0Va5w1mA7E1kEi5dAbaI93bqO2RqhDgF4WAvr+j4nDz+knJQcHt/hzsuvMTs8QiTDGsd0PkVJqKCoREIOu84wxqLzjK5rsW2H8RZrTCjShMd2DZXOUZ4ekALoUjE52KOxFuMb0A02y7DKoJSnyjLaEqx3WELMSBijhmpSkvks5I63FuuDUjXOhngQrch0hree3GumXuMtLC9L1ufPDxGYzuCdhCJLvTtgAwhg2y2alKF1SWlu+RB6y5mhUh2879GOh8g09GcHbYZ3qjd6N5eNhiNRIbswRontkTiWNvMECDEF1Lk+Or4P7lPBlz9kFnzMpx/PT30kgk/gY9MPng0Vn46xbmDUeELCDD6wLeGoMPbVkBkXxEXlKhJAQWIV4n6lBC0qsgqqt+6VhPcmXZ1o4SPSBwluP1OoYBj6waPF9X3BsA97N4yLtVJC37kNiRJAAQnQbIOtsTwVEBRFsXNiHzIE4+27QMJQwaf9uwLwxumBYzfFLkt2/FlrvaXA072GinbMLCQgkM7d5VIYf97FgIwV8fC49EzDYkwJiOxiDYZuj12sxfBeu9iTXVb6LoU9PHbXdYbH72Jk0vanpWyOXQ0fpyzOzsiwzLQjr6Y4V2FMh1ucopePqX2HIkNicZFOwcqEEsSmEbTKsV1NjQWXUWnL/t5tDg/u8v7jD6nulBzu7aMLy6I9Aa+YVwfszQ+588LrTPcPQ/aAa7BtS55PefXVT3P71j32Dm6z9+gWFxePWa+XNIQaA5YOpUqyIsO5hsXilL3ZAdCRlxKqBNpADTsfYkFq09F2NmRRKIX3YTLVmaB1TpZnqExjo8WRF8FtYrsu1jZwiNJoyYK5FAPxRATTOZTvww/C2DUGY0JFx/vvvsP54zMObtxCVRW17Xh08oA8L8F5Mh0mSKUVOsvIpAClQy0Cn1E7R9M1NG1N29Z01lJNCmzX4Kwmz3LwcbITj3dCOSlRZH1KoMoyrA8ze6agLDKcTKhNh/WgFDgraAXaBQa360ywyqL9ZK1HMglVJr1Fa/DaoehwpsY9pzEMxPgeQak0D8GwdLGPSqFXhCopnhRQFgBAYgUQ6V0FvcL10S3gg0pX0eGt+1OC0sMJTnmUk+hv8dFXHV0KpFtvrF+J7RBFyJWHQCvgAgCwqQ5BnD9EUKjIQPgYpxAAgd0yQNJzpJtCxCCbPvK+b2OCNqFQT2QG+v6Lz7Lpun7Mp27zRIs7XkqJ2wJDSgIo2OiZAIK06BiwO2BcVGRckpU/AjkhbiCwDXZg1A7jMxIAwrveBRLiI2JfRLCUmIdn2JLPrlR4lbU5lDF1vIstSHJVul9SLFdZxcPzr7LQgRhV+WROarrWeK2FoYtinKEwZBTGn5/mrhi3/6o0Ga31Vl2BIWDqqaERKBor5uH+4b5dyn8XgBu29SpGZti/499o13OPt+0CNB+XrB8/QLo1xUyCe0BVXHYWWV/C+hyFoygqxHucMbTG8mgN1oAiY29WYnSFE4vRGsknzI9uc/vFN+iyI/b3Drl965g8g+XyAp3llNMKrRydczR2iak7XNugvKesKo5v3aaqJqiq5ODWMedn53z4/gc8evg+rj1HaUFLRb1ecvrwA9pmhb5laU3BrKpADIIlI1gPIpbGOFrjKPKMPM/QWREtLMjLgslsgs4UXWPAWXKtmU6m+M5imjpMdmHeRLQik8A44KFuW1y3oUyN6WjamrqpWS2WvPeND2haS6tn1PKQB+cX6DJjUk0oi4L9vSllnlFNZ0z3DphM9xBryRW0XRvGnnM0dcPlYkFjOqamwpoADsqqotKaLE6nygnTImNezOlkAdohFHSdwdgGrUuUaMoqx9Ye7SVUKnQV1jQo3YJzOGkRH55VlMZ5wUlG5xQahxKwpmG5POX84jFOPWM2/S5KsIptVJrxHY9+fQhKMBT3iUo9AgJRoc5+UIhRCfmr56qhxpAYDOiT3ida7HEgOJHoRtkGBCSlTkwkkM09epdB2pEOci4q8sheaJUYfpz3qMjkiN/4+5ON3PcRiYFI90xxCOEYpWTTmAgA/EBBBxfCBlS5dBChE0LbR0pbbebKpPyDrrD9rZQSMpX1wGyrz6Mx5b2LWQ6xRfF/WklMlaYHD73BG+fyFDCbmrsBBDZFD/SgKLAyV8/FHxkQ7LJIh8pmWMo3dIJ64rwxvbyLTRgDhjE1nSr7jZVfOi4p2PH2xBSMLe9xRsL42kMgMdw2jHOQ0Q+Vvu9a6OkqIJH6etg/Q7BwlVzlzhiDkTErsws0DPt5eMxVBY92jYNdctX2j0Pa++9gvIOFxYun9pomm7A/PcCTMbWOeZHT1mtsW6ONpVYVPp8y2Z9hc0emhJlkZHrO3vQmh/Mj9o4OeCHfo5zl5FVGVRTMb93EOMtycU69WlIvL6jXNQpNVZSURUZeZFhvqd2Kcj6lmOSU05LpfIr1lotTjcKAFdbrlvVixaSasF4syJmS5wWdg8YYcgxV5pkWhiLXrLtQYMjiMM6GlCUVivT4DLJOxcp9DqWgKDP8vMKYGa2xONdhrEHE4cTR2pa2aVkuVyzWa5aLmsVizWpVs67DegedsYgVrM54/PYZ6p3L6GIgMA5KkWuYz0pu3jni5dfu8fIrr6AoKHXGenVJledkSCirSgji+/D+I6xrMc6TKcXebMJsOiErMpRR3NyfM5vsUZtH1HZB3XYsWoXD0LYNndWoctJbbKF4VI7pMhp7xvnFQ6qipMpm4b0QAdGILrBW8Aa0Fep1w/n5KXVzGaoiPicJ84EbKLVAaSND6zba59Fnr7UOqWvRu+O963Pdd7G3u+6pY9qbGxyblFOypj0pEj5eR1QfTT++brh3cA+IqKCgfLiB9xv2OCtCHQlxCWykwPAQ55Eqj0ajPtwL3/vaiT5151MxJOKz908ADIIt/bZh6kkMQgQAfjNvD3qoD/SjBx0hgFGpwV2UYPQmE2MzHwdlr33w+4ciaMFNI3HcBmbAbvSGbIIzE7jagLlNn0f1T1/YeKvtV8/HzyxdPFasQwV0Vd7+WPmNafAhbX6VIhkrzSEYGB4zZCZgkxEwBhvjTIkxDZ4U8C4L3LmwquHwOceA4SpXRHperXUfzPS0vh72eerr8WqMVyny8e8wPmbYn+N1JMaKf3j8EKyMXRzD/htONGN5XqDALRdUezPa84bOdEimmVcGt1yxaiHbg4vFEt9atAupQYtmTZ5VeHJuHL3A2jhUMeP49ou8/OorKA3n9QIljvOLJacXhizXVFWJFjh/+BC6FtuG9ROKosBJAUoodEa9XJN1CjOhT4s7unHAZz7zKb7yZY+xDt+tsd5ydrnAZxmiSmhbVk3Hwf4N8lmJbdZcrC/xTUsmnvnRHpfLBbmeIlLQGMOqNawXKy5OBJ1nlJOKvNCBxkdhtIaq5OLRAx49fMDjR4+4vFiyWjXU65a26WiNjx6EzWppSmUIJSpTkAte+7AgAipMQlohWnAidAJnnef8m2e8+94Jv/CVr/PZ73uTT772SbIaumbJZDaj2p8wqTTLuuP9h47zS0OZKeZFwVTnFKLIs4x8UmLJyPYL6uUe67WgTEc9CWmcCo8xhjyzVLmOvtUGpEPrAuMneHLycg+VT4AMEQ1KaOs1mQ5pn87B4nLN4mLF3mTGfD5/LmMYYkU7UVER+sAO9LEDEJSIjzR2WlFPkWndW/WhQuNwPvCDcwfz0gAkOIkWtg/lcHEbSj1y1Vvnpjof2wpoY7UrlZRwuGaYXze0fqDXBWdsNLroEyvCHNO3vI9VSJ3gvQc11Fv0oCHhAO8T8Ni0DcA71yvrzhjGRrT3jrTSZK8Dnug3AlXjR64GlypNpm1RR1hADNoGKNyXHU5zKiq4+OJ5vY5JgYqejWto09AItiIISnERQiyN/PS5+KmAIJ28i5ofPtyzJvuhBT1WXGPq+iqFJhJyyROo2NW+BBDGMQRj4DEGCMPvY7fH2ApOP8o4R/8qYDRkIsbWfF/MYgfjsKvc85h1GAMD59wTTM3wecaKe9zWccGlXb9BavfYlTEGClf9nh+3HBQZvllQTXK6VqgdGJ2hqpzZnuf95QX6wjDPK7IiQzLF0cEBt268yvHtl7ioO1599aVQXa/MaU1Ds1zi8Lzx4gssmiWPHp9wenrORfOYfJLRNCukc8yrKZkWiqKkyMoAKr2hyCrq1ZrM+BD1XxVorZjM57z5me/lK1/6Ep00tLZhfXlJY9eUZcZBfhNV1yy7+1SzfcrJHDU9oKtr2uU5Dx/XeFXROcOshJkWMm04WVywdoa6banXIQ1wsViyvFxweXHBarmmbixhzsoQyUHlIBWeAtEeMoJ1JoGCtAOFFPRQqAboxeHEIlYBofCPlZDGq4oc4ywfPqx59F/9XVY/UPOpN14FsSjpmBQF07xiWoZKkNNqxsMHH9Aow7ScMz3YZ1IVGBwrNPnePkfNisM2R2vLZJazqlv2pyX7+4c0ncETFnDyXuElrBGh8dw+vB2eR4W4irZrMT4o3kwZlHdoB04p9GSKKibMbx4/t3Gc/MCCGyjbYLkm6jwQBtGChFBNUqQHBc57rMT0YZeI5WCpb64QlGOyPDfvue+PxwuaYOkndSuSWIw478hg/iOUDFdKgqIUH/RmTJPzg1C3kPefsgo2Frz3PlYV3RToYdANyWpm628ESxGNbOsC+r8bN8fAINqyjeI5Mprb8WxOCe451zMwG/CT+qY/L+mKdHXn+mcWkfg++S2QEuZf94QO6Q3PBLS8J/VeCmDsQVQy+K4wSuEZgGBYqGZsEfdddYWVOiwxObaq+44ZXXeXMhoq53FswNBiHlv2Y6AytmTH1ni61y6ffdr3tHs/y3ofW99DRmIINIbtTbKr8NOufhv3MbBl3Y9/p1T6eFd/X8U47Gpf+p5iIsbPuev4j0tWjWP/xhHFZAqdD6v8KYd1HXk2o8xyikOPkhZxhhzFrJjxuS98kZuvf4JHl5cs1g0+U0imEDK8zvFtx+xwzmG+x/HxPicPT/jwgwecnJ/3dC3icQ7atkNEszefkBUK21rmk7CWQZ6BlkAVVkWO3te88sYrnD7MWVyc0rU1vjO0iwUah1Yem+UYLHlVUM5mTA/mrM5zVh++S9uuWZ8tWC0XrJdLFpcrTi+WLOuOurZYA96HySMsjOaxrgBcKDQQFWcIWIuTsmwHi0GgX6NuwIsPk5gPn10MvAoWYFgfwOAwWWTKdIZrOn7q7/wCHsun3ngFo4QOT1Vq5rMcCo3Xjtn+a2iEPMvJJKNpPa03lDl03pJXCuMMtm1oFh1nuWI+PcRbh8T3SufBj24JNQysZCAO5SzYLkzqFrxTWITV5SW2uUR7z7ppkEJRTmdMqr3nMob7PpeoLJPG7y389D2oAR8pdWssKlNYG6tt4mMBmw0YgDQ/AYNfOBTWSeWd6RW2JMgh257oVAU0BBCHmhwJBISA0kTv+/74IXUfL9m3SvSG6vaR/jdxbunnQx/blQABEtclSN0RWRM2x4X1MFINhB7S4gcIwHvfsyypNsKAZwjHJ50kikTQh+yLgeKPfhOviJkasgUqUncoNZ5LU18Nf5PEWA/ZncCYhNTIQVwBkSXSWXzuAYjil8AQDBXW0BLcfqAnaX8R2fLlD7cPlfLT5Cqqe/x5HLA3Pi4pw+G9k3Lede7Y7z98/l2W765tY7bhKkZjrCyv8tUPj7mqH9L3q4o67TpmDCISMEltGfbV8Nme1RdDAHLVuPm4RG6/xFrCqodFmXOI0DlL0zWsPGSTGToTMCu8qVEqI5/OOLx7k3x/QpV5XLFx+ZjG41Zh5b26WZNlFXmmmExz9g8qvLZ8+OAhnSgmKvicPQ7vO5zL8RaMrdG5ZlpVoBXWtmHikQJEKCczjm/fI9M5y/NTnOtYLJac+4a9IlTlO1+3qMtLqCbUdc3Zo1MeP3jEet3QNCaULDYG04XKjKFYCWGS9Fmw6AW8CusRBHtiME5hoGQG43HkVRI2lhb9RBa/pEIqxkYK1SPoAKjKktY6fv4rb7E3m/LGy/dQuaZtWvy6xqHItGU+2+vBVWctq9pjbEbbXbBandLVa3zrca3HtC2LYoW9ZalNi/Uhzau3bpUh06Alo21qWtshBCpcnKCMRTnH8vEDnGtxIhhRFHtzXnjhLtWO5eA/LvEWRCfV1IeKgdekqD/vk2UeFaSLCtB7nA/raYfPPvrWN0rR+23VkyzoXhFuWOpgvcrGXx9+f9cr2wQItFKIznq/+ugO/T3TbbzyPfjwUbGJD1VF7UDPbM3lKe6AGODnAJss5sA8bM+/sZ8SPd9z79vzWYBMbgM60nP7bQPbDV+IBDLieyBx2WPvBBczJkJbkkuA+LyBXUv7XKz0JE8YU9L3Y5hjQ8cp8X21yNS7Sm/WaRHol11Oz3eVPBUQjBXWWNGMlcau83ZZ7E8DBMN9V1nrw2N3sQrDAjrDcsGpVPEutsD77cDI4TXHwGLXs6b2pusMKfRdbb4qPXPXj/WszIokw2fbBXbG+4ftuUrSdcYgYnzeGAyMXS/PS85aDbbDOMfNPc3+bEKWaZqm46Res1isWMdV8rRTWK/IastXvvE1ytPHtJGiLLIiFGpCoZUnA+r1Au9qgp1tqGYFVhzTxYQuVSH0sf65aLrOY10oKWycpW6bSJGCKMH5FiuKfFJgxWK1pnHQrjsWdcP5pafU0LaO1p9gsw9xomnbjmZVs1yt6ToXKxSnyUGCEo7fQEApfPJ/9tT/xtqUNPNvWUWA95uqcIm17Y8b2Fjpst6FGu3hDIQwlowHlWfkZcWqW/H2ux9w+/gG06pEORAylNIUZYnzFuUE5zs609B2lotVw+npA5pmRa4UZAWtCZUJLy+XPD7NybNDVDZD6wItOVpFO9AblCrQEEo2E61BPLgO13Q0l+cYH5Z1VpMZVVGgigL3/PDAJojOJ+o6pOHFrL+ouGR0DhjvgqK04W9vL/vI7kTf+OY93TBBPWuQCgsl1wBp2CRjIawB0IMB53EIeRYXExOJynSAPtgAyV5xI5FZ8FtMo4/3GBtYG0AQG2QFr8D5bssA2YCWDRB4cv5yz9QzuwywJ4zFdEx81gRsQp+ntNG+CwIDknBXundk3Dbgo38hGTYtATZHVPhJ3w70Z2/g7XjmXfLM5Y/HFuk40HCX9Txs8FX7dym45LMaAoJdborx9dO5EGjwtm1p25au63q3R5ZlzGYziqLYaa0O7zMEObtcG2OKffh33Fe72jjsk12KdRc7sotZeBo4eJpFnp5hWDlxzEDs2jb+vOt+CTxcBXo+bjm9qPHeYTuP0OFUxqwSnNd4JzSrFbXLQWUo0axFWJ+uuPzJn6aaz1BlRlHkFHlBkRfkZUVV5GQinJ8XYVlgHX8jJNQC0ArXGppY+jtTGu+ji0YcRQ52bTgxF2ilyLIQjd85T+M9nVMsLpecPDrh8uySdt1gvKMzYblh0zmsFawLtKQi5D8HqyYo+0Dzp3KpMtDgxP1p1xY/CQwmHed7PODFo3xQOX54OR/BRLKj4jHOC+JDjnayfsQqvA7Fj5TL0FWBZDnvP3jEg7NT9vZmzIqKXOVhyWXvWNUrmq6hri9YrS9Ztg0Xq5azxw/xboUqprjO432HdTVtu+aD+5dMJ/uUk1sU+RFVcRhSSzE0zZo8C+tISApoU+CwtM2C9XJNvbpk1a6ptTARmNkjFqs1ZfUcEcHAWN+8/2nuD9pd+n3DuYyY4rd1Amln2JXA4EbxhN1+63tqBmlu8EPlugEEHolLT+vtdnjfj7vebZXmCEn4wvXPsTW/EVMNvd8y+JJYtg2qjZt10y+p9sBWe/CDftttPV9leMJobhYGrrUIogbX3gICQ7ZhAAY2rpD06qUD/Na56RmG/dPzCCJ9MaJx+39JgCBZzEOlOE5d2+XX7xt5BQgYXnO4fXyNZz3Arus3TcNyuWS5XNI0DV3X9cF2N27c6EHBcKGl4XPsat+Q9t4FkhIS29WeXW6D1LdXAYFdrMEuBfs0oLDLXeO97wHS2KUw/j7enmJCxkxBOmaYcjl0OwwrMT4PsTHE5rTxnLQL5PSCTAm5Kgk18jXWQ8jth87Dci2cXLYouQAV6Nk+zkkJWkOZZVRVQTUtQ95/rHCXlxOMs7SdIc80oRiMCmsBaEB5tFasVzXrZRvqpzuHN0Hhr0zHuja4Nkyu1kcLQFSw5CLbkFYtk8iSBgWtek2dwECIQg4TtsTZJ+Wve0mTpRvMLuN04WR+bsDBUHy/L/qmnevLx4ZzXZwYI5gmsCFKK3yjsaViLR3v3f+QG0f77N2uMF2LKIXp1njnOTs/5eGDd3l88iHrukbrir1KqHlA00xp1xbX1hTKMZ3tc3nxAavLbzKbHVBNPwEHbyBaMPWS9XpBkRVkusBFf3tnHNa0LBZLLk/PWCwvuLw4p1Ye6x2zakouGl8U39nB+a1KjBbvQZwPmE0NFGivgEW2FtIJIC1yRAMljd8Q4kNJFjXE4EGJRwRNiohgnRu4G0Icgosav8jz/jowmNMSwIxrCTgbHyTSHcGuspu5LSlONvPaeO4Zft4Yk08q743/fatTt+a8bffCbp22y0gNcRlhPY4tJkWGDNnY1UpEMsM2DtczGBh228hhwBykNm230VhLpvVWWeSPIs9MOxx3xjho7CqL8aoUtqtAwHDbULYG+RUAYbi/6zoWiwVNE+jY6XTKcrlkvV5zcnISHjrLtiLlx2WCr5KrrOddn4fXHjMFV11zqPiH/TF2nQyV9/i8cebE09DhGNg9DUWOX8Lxc6dnHVaKfBo4/FglWbPRR+mco3YdiEKRR1Qf/jOkgLgQSe+tD6DAhfO99XStonEtF+ctsABcnGjjKys+6NW0lLCXSIWnpV0FUFiJa4X4lC0sOBUs/VCKJAtpX2pTPiW4i6O3X/lYiWwTlZ2o3UF5EkARpqsNnZiUeOiQwe8ko/cgmIT986WNkhS/J3z2DodDbAAF1hs8Li4c5DZ+WBeKzDhxOAUqU2RlxodvT3jxcM5xpehWNd55sjJjcVLzi7/4DR6dvk/X1JRMmWSQqQua9VfwcotS3+TWC/e4e+dFytlN3n//Q957929xsfgGrXsHlVW0tuZi+ZDL5n3m3OSouhOyNJSms4ZFs2ZlDJeLJcqGWII9MsplQ/PgPgtryQ9u/5KG4S9JYuGkzYJGkAoM9HN+PHRrniJgxI2LKCrv/n2M4yZeYcui35DdsWhQ/A39WDlvruEJ2Q1pfrLWxuv4Xqe5IfHQuyM2gCNlKgDRJeC2iIpx6vmT7K2iD4h8iiSAvOm97Tiq1Ifxrn2/bPpts8YChNUSEwCQyNolUJWeZXPfgQEWgc/m3Rz8GWUkJFDt/XBrqHuQvg+XAHAu1DtI5z1LPpLL4FtRlknGee675FmKaqxQdqW1De+dGIK6rjHGMJ1Omc/nTCYTPvzwQ7qu61mDMRK86tmvav/4/KvcG8O2JRlmDYzP3fXsw99gOFiH9d2TUh9fY1cmxK7nTJ8T2BlnVVw1Bob3FZEn+nb8LB+37M9KltbiO0dfpCO9SCK93zJZPmFvCt4xYckdF5fEs6T/bU8MXoMPa59J0suOuKiMDffxCuVziJRi30UCXlSCFEBMX5IAJlSyjno7TmGJeGNgtA+twd7i83FHnLdcVPZx3biY3rQ11z4hm+C1OHElNsG7uJyyxRuDdaFGiLIOcRZ8yJxw+HBMX+EtpI558aBCxobUmsuHD3nrq7A+f8Qkr7h391W61uLWHS/dvssrL72MUhrfOjLAuZoX+TTTvVsoMpwxOCyUGbdeusGdF38P99/7Bb7xzt/mrfd/Gieek4tzanPCnfmbmL2GA/MCUOBsh+tWtKcnOGspb7+I3ruDXi+xdk3XrdBd/VxjCEhK2FtiAnv8b1tZJCXc13xRKqHUjdZ9QlEO5tnkY99yHWx88N5vFHi86+aTKLIs6ow4Rvzo+kkRPnFn7xOZFFkBH37PtG94iQhWhuyl90MFb690me5iR8ftszauzRiDUbfSEeMxoT8su+b8dB/vN/ONx8c0wKELemM4pfUMtl9G6eMCtvXBdj8m4OE9pJUZt7rNxcDDZwAk+Ah1CGCbKRhalbuUS0JYV1HZsPEb7VLKT6ODhpb8+PpD6j4tXWytZbFYYG344bIsI8syrLU0TVjbPBULSsfsKm18lUtg3MYxgHmatX1V3wzbkI4bsgxpSeJhAOOwT1P/pO3DTJFd7RvLrhcpHZuuNQYZqe9SO4b9MhwTz0N+4PaUr521PPIdTXSXuMHcaFMEuvebyVJU8LvGCm9hsuppht5K66cTAUSjyKN1sJmwfVLMvdKPN1YSfaY+WnBxV9+GpIpl+z2Ok0FUqyTLZjORREvAx089LvD93qAfXPTnBlYiPH7Ic09sRJigHXgT6s07Qi6zs3hvcc5iTINYh1gDzuBcWCnROYf1LWA3y7amBwmLv6NE40XRGE1bV6xWHW2jeOHGEbNS01pFmzvWhlAtsiqYHJRkXnAGXKbIihLTWlzbYZ2lIVRNnN2YcKQ+w+Thu5x/+D61OSFnRqkP8D4Un1ktz1G6oG1blssLxGbceOEVpvdeopzMaB494vTD91muLiimM1T5fMYwhGWnPRqUgbg0r3dxgIpFeHINl20Gz/dD5WmKYZeL84nV+NI13fAem/vqGOG+Sz8MNZ5sTtoAbCGs1OcTZxd2pLQ9fByDQlSWm2WYN+y6PPEMwz4ZgoK4Z3NMX1EoPVMCBYPnHl3zqrktzYs9YzrIzEhLMvukp62LKxQO75FWTdwYBJvmbgyxFHAqg2dUSmFMZCxE8ErirPF0eWaWwS7rcdg5T6OShx02pLjHyjX5mtPn4TX8aBDuillI/0SEsiyZzWasVqueLYCgIKuq6oMKn+Yr2hU4uUuJjy3pYT+MwcouhTy+1vg4733v3jDG9EDgqlTG8e8ydAXs+m3H7d71mw9/o3H2wFX9smtVy4/CMn035Nd+4Qvw5W+wfuc+XRfAQP8mDq2bBMLCt0HVvXHs9rby3WwOLx6popkiEe3xjVexjny8okhIs0qf0+8wmPi2bifSK+q06ItPWCY9UjRqAmZI5sHmQmF+8wEM+M1WNbDOiBOxxEnaO4N1TVjcwQneWryz4Lug/DsbwYIDZ8BbQllDg/JdeACnNiBEESvmgRaP8ooX797ls9/7fdy4cweV5ayN4vTygqwsQ6S69azrOkTMFxkzKcIaDirkj+tMcE5hOofPc0pX0a07Vq1FZXNydYgxLTqbghImWtPYc5r1JW3rycoZxy++ysuvfC9FcYBMKpquo1YFklUUF6dYu6I9u/iWx993SlysHeC9DzSy98Q0AVLBmmFK8NhIIinjkZEV/qU5ZDOXbK41rOo3nI/DKoh9wGias5OS6z3L2xbr5jqQaPikd9N1rQ8MR3p1RFLdjO3jUw1/H8e5c37z7vlt90f/Zo90S2xNvE4MxKV/WyJbsbnvBrhvHioseiV9cSDpwcQmRic8QzhmWAYf4tzqZeMNGs6/PtWIcJu5wBONWIfWKsSGxKDSNOcnMJJcGN5v1lkYAqCxPBMQ7FIYQyU3tAjHvuOrKv4l2QUurirQc1U7hmxD6ui9vT2yLKNpGowxfdvKstwKKBxef3ztsVxVI2CsEHcp1F3Kd3y98TMnCz9R8MPI2vF9doGK8faxPI3ZGD/L8LcdP/d4AtnFDg3HycctS3FcdC3dYILptSfAkEoloez+wH5aSLKBERIL8Wws8bBZ9XaN3zXGZDPpbFiA4UTdX72/oZfN39CqjUUwsPk3QABiGlK6QLT+I2vgvSflNXmCVSRJwTiH9zZQx87hXRf/Wby1YE0EBOE4bAc2TJppu/gACILqj1rDO8S7EItBqGJYTXLuvvQyn/vB7+fl116nmMxZNYb16pJV0zDVHRoVSkBbi9caayzLboVtGsg1WZGjdRZ+Mzxt06DzirbpWF2cYYyhmNxAxxTGzl4E14NXdFoo9ve5eedVXv3E5zi+/RLea2pjya2hykvyvKKYzlitLjDnp9/y+PvOiQcCw9VrhRjA5pGgLP22n7h/Bwfja2y0bT4PDBtPXDXPo3VkSWM9g/7dHugVFb/49HZY1y/kk6r1pTEXMPPGd95fxG+YCBdX7gsZueM3cPDOqYER5RzeJ1I+vCiSFnsAQiJkUsDDrDHwPdiKSz3LYA6TqF/iMw/LFyeF3befDRBIG4Mijise6lgyXKnNEX7wm6TKklkGBDBnTXC3DX/Y5FKJnRbxTCxXrSQ+X3yv2dg/fvDMV8lHqkOwS0GOZReVMlYEV7ELuxTSVe0ZK5oxwNBaU5ZlzwgMAUtSWLtQ4q4AyqeBkV3PPmYFnvYMY2CVJNVLgE2MwC5LfFfbnsYEXNWetO+qZxz/ZsNyy7t+g+E5CeANgx0/bvn/ffUd3j25oLEmRtcPSoT21nff8B599yhBgOEL6SVU5ktTz/DcHi6ElzVMEElts3EfDKSvXJYmhv7o4UH0ir4/x6cWpP/5wbXT5OX7AC7p96eCJxtK1jkLzqGcwieF7wzOWbwzQfE7j7Md2BAf0AMCFyv9JUBA2C4x1sBH0KMIkdCTcsLB/hGz+SEHN4+494nXefWTn2A236OzoDqDVwovBdaE4LdMCVghsxa7XrNaN9i6wYtQ7U3Jy4osK0Nltrqj9jVNs2Zxcp+uXiK6RJc53q6hWWOcQsmE6uCQO3df495Lb3L7zsvkWUFnLaZV5JJDMSErKrJJRbneo84mH33gfYfFp3iULfQax0FvBW6C77aAQQQEw0ygJEN6egh3fQSLEqsh+QgS/OCopFjjSI8KVBgubhQvliAyIpugwlBK1/fH2AQS0qBOlf6G1r0KSq+HEgOFHJRyVIJqE3UTQLCN774aAAI2c0Cou70NPEiLMJE0aozjTO0Jn1MtkRAzsZk7Nha7RmcarTRKB6WfnsyEamFhVUpCQaE8z8BDZ8D6LvZbMgV8D6b6X8OnuA22GJuUVqIUwT0ZwU5vkOyQjxRDkGQ4kIYKdFflu2E2wtByHSufsaWftg3/jrftAgLDY0Q28QK77rFLSY7TKdN5u4DOVf2yS7Huavu4dkE6X2tNnucURUFd1zRNs9WWXco37RuzMUPQs6vd42e7CvwNWYIku9aKGNchGLfteQGC/8/PvAPK4pXd+LBj7v5GkoUTPssgerif5NJxG8N9cFak+3rlL/3LmiwAejAgWxPZsJLptoIfta5vevqNYwt6tmNgbT3xN97OB0u+LxoU6VBvw5LI1nhwJoCACAiwNswmzkFajc07PAE0eFxkMwMz4CNYCEvWhrK1eZGFxAsNt1+4xfd+9nPcffFVDu/cRk0rPB5rWtbLNYvFEpRGV3Os67CuY1ZVdDRh5cV6zWq1QjlFZwwOKJ0wmZVUZYnJYLVeUa8uuTh7SNus6XyHNWFiV35O1ypUMeH4xmu88Or3cnz7LkqyOGeFxag8gio0Ki/RZcHU7NEd3HjaUPuuSkpr21DzyQUUaGhjt+fPLRgayxeOmQHYuGi3mAeR4VCPc0sYL+Eaae2DMH4cEivhhXNFgR8gAkljL8DPHrtuAEGcF/u5JBQ06p8lIMoQS6XD3G6s7VmLIYh2zqPEjTJnJI798N70uGMLzEeQE5mX4fsUXAnx7Uy4AkhrO4fsHRvf+pRZoFAqQ6kMrbO42ma4SwIKHo+3jpDtBBJLe+tI+St8z1ioGKRvrB0sUBX6TMmm9c7HkGHZVvoh2kSF38WNJpiBPDPLYNf34d+rUsx2BQ7uuu5YQY8V/diiHiuuqxT2LgCxC4hc5Z54Fjty1TG7LPkhANj1HEopJpMJVVXhvef8/HwrSHDonhlu2/Wc6Tl2LY70LHkaqzG893ihpqtYil39+XGLVaGgTqqIkmx78H3WXdr9hB6NqBog1PYH2AQKejYKfei/j/ZBnCjS9XqKoYceW97KIWuVbhm3e/F9Cfv+gDRh+wRAhjuJ1L/flCz1BNrf2/DZ++Drdy7EBxgTXALe4H0EBdaDc2ESdR3edxEMxId3gDfxeuE5lISVEJUIGkVRZbz08ot0lzWN73j5jVf4wo98P4c3brFqFdYL4i3tao1dLmlOHtEoRTGbMy8rXOvRtgtV95qWh+dnrC8v2SsqVnXNcr1keuOYI8nQugqBj53j7NEjvHPY1tG0DYaGonCU6hDncvLDA8hLLpcr9OljJmUocFTkJXt7BR0Z4oUsN4hWiAJjn88YhsAWBkU5AJjiUVr1P/nWnNYrfN9bsdtzoe33b5Tvk++qMWZjjQpbq+uJFpxN8xEkbtr11WFjpc54ce83gcn0pYUDmATXh0YEZUoPMjwh1kSpMMasDe6rdJ9U68M5G0CFDYNTYnsl1udwPqUHD3SWxGwblcf3xPagYfjOgupf8DT+JbIYQSnHeRhLstilB+sqPpvDK4nP53uGwrmQjhyqOtK3Pc81WhXhPkqHFRNjfRKRyOz5qN8l4ThNoDEC69AbGSrFOMm2FTKSZzIEu1BlkqHCu8qCvyqobXz9XZb7WKHsUojjtlwlT1Ps4++7o2M3+3cpwGG2wlVle3cpbaUUL7/8Muv1mvPzc9brdX/8sLDP0NK+qlT0sM/HYG1o6afnGVOIwz4ct3/4eZjumM7fNUauirv4OMXj4prikF7wQM0n9Z5kkzMcFH0/y/ZgINpb/fFhoozX3BhW/XVTkgLxvFTtoLc8RmDgiR5SG63vIxXYJzK5YbCjH/wfYjpAmPRdtMi8A2/x3qA6hzgfIputxbsOCJS/cwEQBLDA5lxnwHc9mIkmHhkWjyLTJXsHh9x+4S73Xn6V23fucXlywuPHb3NwVLJ4fMpiWZOjWZyccjibMs0OuOhajO0gy5jN5nTLNQ8vzqmbhsIozHKBzyGfVBzO9pneuM35gwc8eusbLJfnlOxzcHwEtmNxcUaeaRYXj3h4/11KLPt7B+RtResd0+mc9tJz/Mox+f4+XpUslqtQWnruETJUXtJiwXtstKREZXjxGMwzx9t3S8L7H5S7RGXSL4ojUdn4ZHCEjduL5qR5LbJCfvPObpZX335P+zlgYE57u53Sl6z+VPRnOI7D6y9bbjGISwGneVBlOC94J9GyjWDbuTCyVAA9Sm8MHBfbqyOQESWgPNaazfLQPmzXEpdTjrf3RCDsARG0VugsR6HxsU5IcHsld1uaMyxKD+YOIUL+oHxdXOwrIXfvY6YDFmME5QbP5kPmje7jK6DrDNZ4smyjS0IAYrQoFHgRdKbInGBMYDFCMnXIikprG2idhTEdV8b0KizaJZ4AXPS3CQiSNThecneseIc1B4b/kk9/uG0clb/LFz22sMdKeJd1nP7uAhbD+wyfY3zM+NiPYmGn/tnb28Nay2q16qsjjq85Pu/mzZv80A/9EF/60pc4PT2lruut5x0XGtoFVoaS9g/Xbxgq+OG9U7t3sUAflVnY1d/Pkw3YJaqfnlSff4+kECrilqH1Hv9Giyep8I2iD2rcJWoVwgQkoXYAkXkIc0lkE9i4KOIUzObLFX3dNylNPuGzJPpz0NowyRNnYL/x+3vfAwKcw9oOnMWZECfQZwb0TEGkPSOgILoHXPRjwsC4EMjykoP9Az79vZ/n87/mc7zw8j2q/UOyaopWOc1lzY/9xb/Ihx++w4tvfIr9psOahg8enXF054h9HGbRUnuNkhyV55SzGf7kIe7ihFU55eKD+7x/8iH7R4ccH99Eq5y8KlhZw0lTc0sd0VlLs17h20sW56c8evyLFCjmeze5WD1mrVeghbVoshtTZi/cxNmSlbGYukXUmiKrwmqUGdi2RumStKaCyjNEK8xqA9Y/brHWosThRYU6DwTfsPIp7bgbnSG9Qg5G24YVCGBCDeb2NJ9uzt68w/ENGAGFcB2J1vSuAOnIFngTFgCK1mmIQ4gxJvE4kZCBE0J1BgaiRMXtCNdxm+Xdt95bT7CKlSbQ/TqEBFhCMacErEfTkhKNjiXLpX/mbcie+lKJkKkstnPIgMcsA6UQpVGi46OH7eIdggueN6VwKjyriITFs4zFGou1DqVSRkdgQwLTEOdTl+bbsD+4ZOJvbyW+qpFP8R6tHCr+17UJQILWlqdN789kCMZ+7uGPfpVLYcgcDKPPkwWdlNVQ2Q2p9SHrMFRsuxT60DWR7jPM00/XGyuvtH8YJDfcPr7PsJ1jwFIUBVmW0XXdTiCUZVm/1HDKdrhz5w6f+MQn+Omf/mkeP368s6DPuIBRkvHzjVmHBAp2uRvSM6d1DMa/bfq+6/e9isFJ9xhmQgzPf54gYV5NMA46b3olHVsVDnDDeWL8pvitfb1F7/sptPcG9DRtDDpMyj6xBptwwZAaNTA+NncYAthBE/v2Diy44YSN92HStdF3Gn35vlf0UbHHWAGfAgOdwzuDuORzdZG+jLOpT9UOAtBQKGbTGUeHxxzfus3RC7d54ZOf4KWXXuXWjUOm0wqHxvoQOFXNC37wt/0mfvJv/A2sU6hqgvY53brjm2+9yysv3mKqp6xXDca3iPXU60suLh7z7odvcXjzGHN+zvrsMVW74PLslMuLJWs8p6cniBjO3m1YnT6iqKZkeQbSMVGWi/qcqezRdTW2bQL4xWF8C96SZY7MQ2c99bphkV0wmRbkukKsw2GCTzfPyIsc6xzVcyxd/ODhfTJdoSQLVHBUxjoLoD5Llq7qSWKGC/lAmq82QGB7DtiuujRkI9NqkT6yRUG5hPU5QlR7YCesDX8FQetg5Trv6Fwslx7TJL2tMd2S1rTgFUU5papmiMroOhvZi2hJS0qf1midhUqoEqC9VkGRixJUpiAGs4Y4Fwksg/WgU/pkCjiMDIfSOJ/hbXhJrRvqAbtVMdFrjTEWj+CtwxiDtSZmEIDOg6tMRMfAR0WmBSfgtYB3kenIkCzDI1jj4r/ATOR5Ht1CBu/D0ss6whPrDG3XYa0D0fjYPqWy8O7G1F+PxTqLF4VWOegiMijgxGBai8quVvvPLF08pqiHSmBsYe6im8YKdZwrO1RsQwU1ZhOSwhluHyr1qqpYr9dkWUZd1z0NlkrpjpcF7mmZHXBp+MzDz7ueNYGXVB0x3XP47ClzwFpLURQcHx/z8ssv8+Uvf5mTk5MtIDGWsWIdU/XD/hr21bCdu1wXQ7C2S3nv6p+x62HcJ1e5dJ4nIHjzZslpazivYd1ZTFyHva9HNDh2FzAYfwo1BFRUki5WCetr/wWmIF5rnNG84QiSC2N092EfRSYAGMQp+M0hPlGjwUJzPqyHID5SojFDANeF2gHeRZ/jACgkl0J03vroNkiBgZHvoMhLXrhzixdfeY0X793j8PCYspwgRc7RKy9ztLdPoUJ9BO/BO0unQKuMwxdu8qlPf4Kvf+NtnFMgOavlira9YL085bUXX+fs0RktgcPplmt8XVOfPOZ0tWC9XOKbmmVrqNU5Z+eX2EzjRYM3NK0Bq1AtOA2NXdHsCZP5PtYY2qbBWYfyiqZbQFFweXFCkU0RPUVcWA77cmHpupoyz6iyEmMtudJYZ6ALc12uv6UY7O+orOsFuCbGZwQKPcs1mQ3rZfiiCEBOEnUdLO/tuKXEDmyeI6Wo9UGq0BtuycVg7dAAiRZotNSVUlhnMV2H6QzOOzKdM51OUUrR1DVN1wa/t/cIHtctWCzu0zRrBCGvZkymB1STPUDTmWgcRowdovIVWZZTlmVfmS9Q4IFWF2VwrqNuasqyQomm6zqs7RAJVjoSgIE1QbkKQp6X5EXJ4Y0X6DpLlhVkWR5KPLiW1nQYE1xqgaZXiFex8FKou6HEoZQnyycoVaAkAJc8C6Agz6CtF6ybBtEZ1XTGZLYHXmO6tPaB4JwhqJBQX0ApMM7gbMPp6UMuLy9wHmbzfQ4ODsm0YrVacnZ6SlkUVFWO810IpG0th/vHVOUeojXGtxjXoLRGu+rKcfbMoMIng1G2reldSiP93WXBDq1s51xv7Vpr+9UJx+wCsFWUJynedK08z1mv1z1V37btFrOQBm5CxelfKvqTniFtHz7LkJ0YsxTpmYAnAEh6gYYrChZFwWQyIc9zHj9+zP379/t7XBVrkTIlrgJmQ3laG4d9Pi68NP7d0v3G7Mn4N951/+Hv/MvBdfD9b9zjwWLFVz444eHlmpXzwwDkaKWP/fe7OTXvB7Z+b4hF5Z7KDcpG9Q+v1N9BBtELQ7fAjttv0ZaJHUjHplLAbqPEvRkwBLFyYPocQIMJVn8EBN67fh2CUJYglhjGoZWiKqcc3rjFy6++ymtvvMrNF17g8OgGk2qCoGjxlLMZZVGCNcEPGvvXiscpD0pxfO9FVrXh4eMT6vUSvGdxcsblWUtBSV3XrLoWZz2+s/iuZSaK9vKS1XpNJrBcrxCC8lFeMB5yVeAEMsnw1lN3ls4ppscHzA9uYxthOm0xbQ3OYkyH7RoWJw8opGK6d4zOJ1jJqJ2hXq+5X97n5uEBSKht4DxkmYeYCvy8xHRt9N8HN0bmNaCxNqyWqQSsVT3IFVSwhH2gkTfvsETLPr7bKgXrDXxBBD+/s8nyD9ZwAgMAITAvfLbptzeBhcszS17kIMEIMm1D1zahRLRtsd2C1fKMzjSAkHVtABRtQ1HuYZzGuA15L0Sjypo+MDAxXkqC0kW11OtL2mbFpCrJdIYxHU1XY02L97YPcrTW0bYd3kNZVsz3Dpjv74VVRE2NL4oIihrq5SV1U+OcxThBJI8uBo+3HcaswTVY11GUc4pqjzyfkmUFXabQytE2F9TLM5q2Q+cTpvMj9q1QFDN8XNvD+5ZOQiBmluUUZYVWmrpecnryPmdnD1ivlniE1XKPSSl0nWO5aHj08EOyXKiqDHxH165p6hZn1uwf3EQrxXJ1xnJ5hs4rynLvynH2La9lMPw8Ls043D+0ZJNiT8oxoc+u63oQYIyhrmvauGTsmHpO7EACDklSql66R9u2WGt7xTy0WFNZ42SxpyJF6VmrquoVMOwOiNxFl6e/w3um8/I879MH5/M5RVGwXq95//33ybLsCcYENop9TO0n8JDOGx+f+m1YtWwIBhJA6UteDq47rtcwBH9DRT92v+xiC4Yun3H7Pm753k9+ihcXC04uf5bzxZq1DwF6saGDQLzNti3xu76GWbf3lY4ARO/b7FmADRgIgT5qe/qNIGI7V2D4eRMYGIIJPSnv30fa33sTAYHbsAOJPrUBDPhYLIhYQAg3ABVEWlMpysmM/YNDbt2+y2uf+BTf87nPcvPOzeDOtTYEamkVqqT1ekbRdu3m/XceqyxoTTY74KXXX6duG+rVBVpBu1qzXl/yNt/k6PCAdnnJumkwFpxx7M/3OH1c45XglKKzjkyE6XSCUsJ53TAtj7AISgutd6yNR6mKaXWHLN8HcRzqEtus6JoVXVOzqi9xqwXWtXilUROP6CrEfyjFowcPwTaU2YSpV1RxZcmBn+i5iOkMirRcMTG6P9TWcDYPfmwZpIL3y3MGN4HWOroawtgT0/XUdhrHwznE2eDXDuyAxUZQCQn4qwggPdYMliOOc7VaKcqiCOebBtNc0rZL2maJty3GtPG18LiupfMLXGdwcw96gvWhYmFYPTCU/PWuwNg2prd2dF2LEkWRa5RYzs8f4W1Duwrj0zlD2za0XRPAQ5aTFvYwJqQQma4k03B58QDTObq2Jc8ysjzD2o7Li4toYCq8KlFZcNvgDLZbYbtLTLvEWENWTJnMb1BNDynLKcYqrF1z8vhduvUFIjlluY/3CqVK9vcLqqIiV4aubUOsjgNrclpvybOC1fKCd9/9Rer1RWD0ROiaJfvzCYvFmrbxrJdndK5BiIG/rosgrqVuFygFi4vHXJyfoHRFWXybgGBs7SeFNNy/izVI9fZFpFf4QK/I1ut1DwDScSLC5eUlsKnDn5RaUt6JWk8AIEnTNE8U8Bmmx6X2JRBirSXP8602Oud6mivLMoqiCPnIA2WZ9o0VYAp22aV803Hz+ZzpdEpd15yfn28Fa6bnSsemZ9Za92xHlmVbbMM4hXGY2ZAAwTA9cHz9tC2dl/pv+GxjZmFXQOOuc8b7v50UyO+UzG7cQR0eUnz5awQ23eNjEFHgHUdM/eh8GVD3waiXnkrvOdjEEsT9mjQxp/1EKy0pFb91TQ+brAYgIRYf27hZUMj3gX/WxQJCNgICt0nFIlUK9NGFYFP1wG4ABFyIZ/C+13VFFjIFXnz9Nd74ns/w+puf5ODWTVSWUbdtXLPeBr8oCmPB5zmdCsrFOkjpnWHVSEfpfVDaZcXBjds0dcPF4/dwaNZ1h33wkHk1wxloG8u6s+AVsxsHmItzCtdReE8mwUeuywIRj6Jl78acpvMsLhesuw6jFcobVqs1PteIVszKium0wpsD2uUSf+LZm06odE7nHWa1wGUdqqgoywm2rvnw3Utme/vcnkzJZ3O0tzjLVsrdxy3WtHjJUCqMB2M3rqJGKTKdk9LpNkaahGVwVU6WFaH4TZaRaY1RhiwPc4q1BmQzt6f523YWZw3GhXHlEivlfQ8O0+uhVGQkJCwn3Zl1KBJXFJh2RVtfUK8u6Lo1eaZwxgbjS0LdAudbTNviFoZqtoexLgLfCDoQfJehVI73HcbUtG2DEkVVVuSZoq0vIbXX2/698QDig+tM4jLNMT6ga+Hi4pS6qTGdY71aAZ48z1BKMLZDYpwD+YTMOUQ0pl1TL8/ArfAuMB3GNCH40TZ4u4fONJeXjzk/e0AmjiKr0EVJKS2zzHI419y+dchsmlPXU5Qm6seO9cqglMaYnKoo6WqNiXUyDA3377/N5eUS5yW4OGxL3axwpokuDBXufXES4hywMbjRc/GU4NiPtPzxMD0tWevJbx+CK8IA6rqO9XrdLzcMG6o/KcA8zzk5OemVaKomuFwuAZhMJk/cez6fc3R0xOnpKQAXFxdkWbalqJJS3aTQbFuvyYef2p9AQAryGzMfTdPgnCPPc9q23VLGybWQrl+WJW3b9uxDUtJVVdE0DbPZjLIsOTs7o67rJ9IBk+JPsQ1FUWCtZb1e98xDkiEIGcoQuAzZmV1ugfF5w2DA4fZ0v3Tt4XoT4+uPgeG43c9L3n30kJ/+hZ/jK998n7NVS0eGR6H7uX1jwY9BS2/8JqXfH5n2DdOvgu7vgwclZCg4oS85vLmw70u79kofttODo/Xk+yAp30f9B/AWUwT7GgIhgyD4/210AwRmIa0+KINJMt62f6r53j6f+/4v8snPfZ6bL7xIOZ3ivWe1WqIFiizH2wCmV6ah9Zbp/gG5yTDK4rwhGF2xhKoOPuvaOJTKcFozOT7idq452JvimhVFnmNMcBVkeUU+EWpdYyw8ujjHiGNfK/arCeu6Y9F0IQAs1/guWpZW8Dqn0AVaPO16xenFCT7XVGWFVS6sWZnnlEf73JxUTMopRaa4WK4wqxWmXuHWC7wucNawbBrWXUtxdEQ230NlOeLdqJjVxyw+UPZu8G4DOBzegulWIcKdEDughoVpdImJrKoIfclfpRRFUeCI40UCsA2LVoUCODgX2Aif4lBi5b7oj08XlVBBpyfYutYGX79NDE1gBnIdCgeVuWBMOwASIU3OmAtM3WJNC4T8fiG465xkGBeyZRw2piYKtdG0KsN1La6zm3GdineID0F4zqGy8C77uBKjOINpHaZdI17FuAQCOIl1DYq8xLRLlIRluwWFtQ3O1zhXo8SGgEln6BqLtSua+jFFXrJeLqBr0GXFK/fu8ek3v4fv+dRnePPNT/Hya6+SFTmPHj9mvao5Pj7mxo0bVEVB0zRcXiy5/+ghb7/9g7z1zW/y1ltf5513v8njk0csF2dYG9KAl8tQM8Tb4H7Ic43SEuKHnKOzIeNICShlsE/Jnn0mIOjH4w5LcLVakRZSaJqG1WpFXdcsFgvatmU6nVJVVe+v11pvWfpd1/UAwBjTK0WAvb293gWwXq/7fV3X9YqmiL6eoZshWfCJCUgKfz6f8+DBg15xZVnWW+FJkZdlubUoBEBZlv3ze+/7eyXFPZlMaNuW119/nbOzsz5bIDESRVFQliUnJydbijrLst76FwnrL0BgC9IKjdmOaNCh0t6V5z9W+mMXxlCGsRFXAYwhEzHsk+GKjMPYjuFxvxziCP7kf/iXqFuPeIUXHQLzMBgETUq5GrZxk4QY9oTwwTTTbZT2SMsPYhHGV4ToEhgAjvA5FkdiO72xP9dt/P0uRf5bizddXFPAhLUEnI2LD6Wlh1P6UcwQSBkDni1mQinNfDLntU9+iu//tT/Mq2++gS4ytBOccbS2xfkQCd3kHlMvWSxO+eDRBzxcnPLCnbu88tKbvLeoObxxiFKasppR5tNIS7dcrBY96Fg0Na0Hk1cYp/CmZjadkZNRNw20DUXXkBvL/fUltbRUxrE+WSPOB+tqXuCV5mK9wKFwXVDeojQ5GeuLNQstFIdHKOPQyxqXZehChXK23rGqa1qd472iKAvKMkO6lsfvvItratZA61o+KAuMcxwf3iJzmr2D/W99AH6nxNVhHcpYdRDSu+7DQlwuVusXiemuab0Mj+1WcfzRjzMVAetKQFR8D2KcSsrlz1QIlu0ZScKt06LGaTiLljDE/GB7dHF1bo0ShxaLocO4DuUh0yEAzjvwrcKjUUqwGFoXfP6pqJZIfF+UDoWHnA0pl5mmj3FoG5zzuFilx3lw2JjLr7AWlBacBZ0FwOpi3Y1Uf0zpIqb7QXC1JOYuuOBM02LtGnwohaxULO8NhAJFYbEvZVuwGb7L8daSq5wy32e57Pi5n/8qv/C1tyjL/5K7L7zIb/yNv4Hbt+6wXlnW5/e5fLTk+MY+RSFMC8WnXn+Je3eO+eIPfB/r1YrHjx/yjW98g5/92Z/h7/3cz3L/wUNc69AiKB3SEdfrGo+jmpT4sHgI1jlaEwyGPLs6W+YjVSocsgQpaK9pGpbLZZ87H1ImVG9ZJyWcFhlKitI5x/7+PlmWcXZ21iPVabRIkmWZMgUSkxCoLdvfB9hSSsmCT0opKfI8z5nP55ycnJDiBBKFnZR6qhSYrPc0sIcWblLQVVX1LEOe57z++us8evSImzdvkmUZl5eXfZsfPXpEURQ8evSoBxpVVfUg6Pz8HK01k8mEruv6GIrUtl0LCo0/j90XQ5Zk7O8fg4ld+4fAYggI0vXSb53n+VYbrbV9/Ea6fipgNGYfPk5Zdj5MjonTT5MosdZAb76Hbam2l+rjC9LfoK5TEGLasgEF44oGcacQArzibfoSxPHU9M/5Tb53SBl0MR3Q9syAtyYAAdNBZAd8X0fAhuO93VwrxhoIfpP6gKAFDo8OeePNN/nCD/wQe7dewOcVzgmZDcsiWzqMrTGNJVfCYvmY89OHPHx0n/cfvMfZ+WOW77zFh7/w8ywpePGVl7mslxzdvMvrn/gsN49f4Ku/+DW+/vWv8OYbb1JlJev1iqZe0y4XrIzhfLHi7myPtuto25rzy1PO1xfkOiMTxQRNOa8orOOgLCnKkpWDxdpy+/Zt1m3N6dkZrXUU1RRdZeTzCdPZjOO9PfK8RHuPsy744EXQymPMmtavIzaKVSC9p9g74IPHF2hpKEuFO5mxyisKA5NqznRv9h0enR9dvK3jinYhDkDiipepMp9PGR6E3zgp/16jDgBsUOqQXFrehUp6DM7Fe5L9ItHSFu/7XHcIRXFEBG82gX+iVBiLIuAsTW2xrgsxLITPSoWaAaJVZABCMm6KZ+m6uCCd6P4dFS+I9zRtA1qDAVEuvnMpeDK2X2dAqtLpsd6F5/Xhbe7aLhAS0R3nHDGiP9YwQBIKCW2ACDw6JJT4gTiufOxf7zMQ3bs4nPUYZ0MR0M4imeXmdI4qClpnaVrD4u13+Mb/6T/g9dde5zOf+T60Dm7sTMPB0Yx7L71AWU5Rosm0JstKXrh1j5uHt/j+7/sCy9WKL33p5/h7P/f3eOubv8iDBw+4uLzEeY3D03QGnUmIHdG6z854ylIGzwYEaSJPE3xd171LoG3b3mWQAEACAakef1VV7O3thXSRqGxnsxlVVW2l3DVNQ9u2vZU+tDYTaEi+8fHiP6l9VVX1yx2Pc/GT8kosBWyUXlmWfYBjAhPDeIf0N4GMxC7M53NeeeUVjo6OcM6xt7dHVVW0bUtd18znc0Skj1UYxgGcn58znU4BuLy8/Ei0/S5afmh1pn1XMTvDa+76PHQfDIMSx7EIQxZluD+BtWGtiXQPO6I6Py7xbqi8B0rbR5U8UOjpuBBC5ukTBbe1/MaCj9Rpf4APTIBPdr6kjASJRZE21tWGfxjEE0Qw0NcIsHaraFBgAjqwHd52MXYgBhLauLpgWqMgLS3rN3EC3gvT2ZzXPvEJPvXZz/D6m5/gxvFNqvmcdd2yurzg/skZKsvIy6JnG+6fPuSD99+lUIrVcsl6uWa1WCHrhst1jcz2uP/NltXlkvO332N5/z4vfuKTPHjwgPXygtXFCQaPMy2u6zDrBq8VrqwwyiPe0rQ1XV3TrmtarSHPmcxmWA8GzcJpdOcxXlDlhBtHN/n6175KiycvAsivu4au8EwnOatmxUTBJA+lX5OnJM8zOmtwxoINVfDSL5HPphzdfZH68iGtafAXJ6EyXObQM4cUt74TQ/Lbk/5ddbGW/abAjE+rBUG/YmAIIEznCnFN4IiJZaBAPaJcP9bBx74K4Nh7j44ugQReU0vCMsW+Z828izX3QyUhTBdWm7XeYbFhrEvw33emocg0XtGvnYH4SOkHcGPx4FUAA4RYA52XtJ0JNfv76n++X9nQQ3hv4r08hDibCP6Cd2Lz7ol4JAvgxBr6okPgUTqy5BJSNZ1LKxcS2oXCWYd1giIjU5q9+YyDg3329mdonfHo0RnOCuu25tHjB1gXwIlSGbnOUMbw6PEDHl+ccvfuyxgH5xeXzPbmvPDuh5TFlGk5YVaVzCYV06og1x4Rx6TK+b7PfYY3P/ka733wAf/fn/wpfvwnfgLrBVxITVVKQv+74AISpWjM1T6Dj7z8sTGG1WrVswLJ6k9KDqCua7z3TCaT3gpOvvXDw0Nu3rzJe++9x+3bt7lx4wYvvfQSDx8+ZLVa4X2o4V9VVe+CaNu2V05NE4qLJAYiKexhIN0weC35+L33LBaLLas/1QlI56Y4B9j2mSc2pIg+neQyODg44PDwkKOjI1555RUeP37MBx98gIhweHiI956HDx+yWCz6+IEhe5HnOcfHx72LZXj/JMNYgauU+NO+j8HEWIaMQlLqHyXVMLE86VkSGzIcL8P7jRmMj1+G1QAkuRSjf1EGmMD3f4drf/j0v4He3/AA6ax+B73Nvzk4wQo2Bw0m5shUJKvLx+JC3saCQjZa/jasLBgAQctWhUEfmACfyhVLgCV+YDUppbh1+wXe/PRnePVTn+bWvRfZP9xH5zkq0xRFzhrH6uIc62EynzOpCtq65sGjD7j/6ANmWYntWnIPEzKyzpMV0K1WnF0u6VoLckm9XnO6OMO6YI198N5bVBqqTKMFujpYi1lZgAjOdbSmxTiL14LRAeTozqGcR1RG6yyK4NbTZBRFhfMKcoXkwYpzJsRT1OcXOOvQOiNXOqz0Fn3GYQ15QXuFaMjyUD8+lL0VZgczVqtL2vYULw8RLvDuMaIXvHz3xe/AePz2xFgbCvH4kDUAqRJhyprpKaDwf+dD7fqeoRrOAT5a/IFKTkAh7QsAymN8KsXlUS4aILHcLhL87S6u1hcMZdfT+z7GujhrIyAIIDUw/7FwT16gFdFKj7U0YjODQRING+/71TN1FgoUuc7jvfRrkKR3UqlYGrkvMpTaG8CSJJCeXAIieByd8VgTGUQfy0LHVGLrHEIoOkTM9AksYkaWKXDC4fyQz3/2c7zx+hvs7+/jcazWNQ8fPcZaz9vvfoP333uHi4sLmrZDZTllUZBJqIz5819uWCwXFNN9FusW//iUR6cLjg9vcri/z3SSM6sKpmVBVQgKh1aWalJQVhX1esVqvQrzcpxNwrRhElfY92f4zXbLMwFBYgDW6zWLxYLlcvlEzYBk9bZtS9d1PaV8eHjY+8O999y5c4fT01Pm8zmvvvoqEPzRiWZPqXghQjJkHKRYgPU6RK0aY3oQMnRjjCvvJYCQYhOGmQvDIMchMMjzfAsMJEZk6GJwzjGfz7l37x4HBwfcuXMH5xwPHz7s3QhpCeb5fM6LL77IxcXFE/3VNA2LxWIrQ2GXch9a2buU9C7f/1CGin+8f5hFAJvKiMkl8jQ2YdhHw1TGq1wQz02ikt4i9IUwoUXdnSCDj1TpMOVveKGektysKDAo5TtU+PG4AVbYAgL9NdMEvqH3vbOITYDABCBgbYwZiOyA63o3wYbrDYWS+oJC3vfsRFYU3L5zl8993/fxie/5HvZv3UGVBcY7tDW4OlRPRIRcK9rFiqUzOFdRL1ecP3xAs76k7s7IVVDq82qCtIZCcrr1mrYzGBFQGWaxZPH22+g8WKXL84fsTStuHuwzyQradUPTLFA6uGaMtXTOYlWGlHOyIqMq95jmM+rLc6xvaZ1BvKA15EphvUNpjWhwKrIgOHLraZdLZFIBGmsc2gcWxuEwxlPmOVoplERAoARnBNNZVFXg0XTGoPJLVGNwjx/haahf+sS3Ovq+Y2Ksi6payGJN/bQ6YFJ+3vse5Kb4Au+DkkySwGlPSuHiintPHuMiCHHehvdEgo/aex+C1sIFNjEF0TWlU7o3YL3vXViBek9lk0OZXyUCyoM4XDKMErXtB3OX94E5Ux6RkAHg4wX7azsfFDmJSVUh0FIFNqN/Yh+YARUBTqhp4LbWU+hf3fBaBnCk4yJGEYiJ8qAzDg8P+LXf/4P82n/gh3nhzos45zg9O+Pk7ASlSzKtWa/PqRcLbG0Qu8L7kHprM8B2nDx8gKA4un2XrNpjuVqhyNibzGnaHEiZFTnzSUGRwdnpA7quxVjD2++8wze+/o1gREfg4xHaNjBJWgmiJYDjp5C1TwUEKTVwsViwXq97ViBlFAwjz5ObIM9zlsslh4eHHBwcUFUVi8UCpRSz2YzXX38d7z337t3j/Pycu3fvcnR0xOXlJTdv3uxdAgmIpAj8lIVQlmUf1JZeAmPMFntQ9IUlbH9OAgdDt0ACBCmTIFnJCYQkZsJay2Qy4c6dOxhjuHfvHm+88QZVVbG/v0/Xdbz99tv9ucYYJpMJt2/f5u7du3zzm9+kaRqqqqIoCpbLJV//+teZTqcURcFqtXqmAh5KUsbDgL/hsc9iBxLQ2GW173IvDN1GQ8ZiWPNgzLyMj31uIpF2HPgFQ5S/762mlC411NbRUAj7B+6E4czpB321xQKM+zXNLD0TwGa52KjIfVo7wDmciamExiApVsCmFQi7WHDIxBoEvocqkIq2BLpXRJFXFUc3b/FDP/Lr+fz3fz9N29GZFoUj04qmtqxNS1ZWeGuY7k2pmxXnF485X1ikc7SnJ5S0nCzOyXVGVUwoqhwjKiiTxpBnCqtBdIZWOc50mHZJvV7Q4ukOD5hXBQWKdrVkeXFGPptgXYVxIMWUIpuh84p8usfNOy9yNDngva9/iZPTd3C+AeOpVy3VXsFifU7nV4jN8JKHmvXeMZlU1N6xd+MGVTVFGYdzMd2yr/SoQyAcFqdUrAqnyLCUcWVDtMcXFpfVYGuWjwve//pXfikj8ZcoEkrVisf3ZYGjm9F5tFY9COyBPj64nobrDUSUmlyCxDEovfLdWNKbO4f3xsf3w/moWFSqhtgHG+C8x5kuxjAE2n8T00IPVtrO4J2nzDOUVljnaZsQoCe9dR4s8YBxQrtNa/E4tA5uX5ewQmQZsJEJjP5/nWUIEqxiH+OrfHAdBHdRWIMgC/wFmRrUeIlZM94JTsI4ynQADc45MI7pLOfzn/scv+t3/E6yfMr9+4+4/+ABp6enLJaXmM5wdLCHaQ03Dm9Q5RPqdc26XvPBhx/gbIdpW7zxnJ+eUlZTbs8PUNMqltJ2uK6m9QqsRmMwRQBVJ6en1M2at7/5Nu++9z5nF5d9v+V5QVYUgbVzoaATLsYCXk0QPB0QLBYLzs/PWS6XWzRySvlL1vfQQj88POTOnTtASA8UEQ4ODrh58yZt2/K5z32OH//xH0ck5OYfHBzQdR0ffvghL730EmVZcnFxwfvvv99f2/vNegCJtk/KLinS5GoYUuCJis/zvA8mXK/XeO+3shCm0ymLxaIPZDTGPOETz/OcF198EWstL7zwAjdv3qQoij4bYn9/v792VVUcHx/z6U9/mkePHvHSSy/RNA0Aq9UK5xwvvvgiTdNw//59VqvVExT+kxHp226E1Pah4k1sQQIFw4qO6fxxBsCYQRiyK2NLP4GsccGmdO90rWEcwvgZPm4J4UXp3RaUAi2gfEfmDWiFyyq8xCVKXVAadthcD8kV4IW4etumIsGWy2DLexDoh423duOWCJRtsOhJwG4QK+Bth3PBNSAusQOOvvqg35SCDW6NRBlvlMJsb597L7/Gpz//OT75uc9CXmBbQ9O05M7hvOXBe+/wwf0PObh9g4MiZ93VLFc1y8sLlqePsc2amXZ0XuPzikI0OE9jWrworDcouyAv58wPb2OsZr1aY51lOpuj0awXF1yuGhatocgcrQkAaHW+oFUV2WSP23dfYLJ/TFZOAk2bKbQTbt15GeM6pr5Di8KvGx4/ekjbXuCcJe8ylCtDKWPJKPIC5ywHs30yFBYX/OouuApChLUEPysCXaxZkmmmezPsyrLszlDlBC8a45co3VLojK999ee/W8P0mZJL3lvKwaD2iHVgY7R8jClQKJRXUakGlSyiY2yBxIW3NqhXElsGPWgIr+pmxT+tNBs7PFDo1lp0lsXYGB/ofhc+G9vFucmGKPzE2YsAQfmLh9a0zKZT5pMpbduxWtYggjVd/z6ICEprMpUMjhAfEAzGTaEmwh2wxqOzUHzJOBeUrQdru1jN0YBApoNizGPWhneeTAlVkdG2hrbtMDYUEbMR0Kg81EMIgQ+K2WTOF77wBX70n/3n+ODdh3zjra/x8NEjFsslTdNg2oZ6seDiJOdicUZnOqoqBL0ul0sWy0uWizOWdY1z0FlL9jhjOpty79U3WNYti+U5Xa3IlaD395B8wuOHZ7z7zjexpuPVV1/h8OCQ1brD+YyLywU4x2y6z2Q6oVm1dC04ayJL4kGuTgd/KiB4/PhxT9UPq98lH39SnkBvHS8WCx4+fMjBwQFvvPEGBwcHHB8fc+/ePV5//XVu3brF4eEhk8mEr3zlK9y9e5fDw0OMMXzxi1/k4uKCd955h/l8vlVTIAGBBAqGinsymbC/v8/p6WnIqx1Q2QlILJdLqqpiPp/37oik7JNCHmY3JIt3MplQVRUHBwf8wA/8AE3TUJYl0+mU4+NjTk5OyPOc/f19zs/PERGOjo54+eWXuXXrFq+99hpvv/02Dx8+REQ4Ozvj5OSExWLB48ePyfOcvb09Li8vnyhQlNo47IMhOEhW/tB1MrTqx1H+Q/Zg6DoZA46xG2F4/8QCjAHM8PyiKPrqk+OCRx+3TKqCdWOxypEjzHXG3WnOp+/d48a0ZN12vHu54INly1ltWRlHZ9qeUt3o+I36d2xPQolccGqzDUAUcf3zeGaiPtMWH/nIuLywN20I5Ou6yAR0hMpjKWbAbRrQt4bB3/BZZQWvvvYGb376M9x77XWK6QyV5xR5jprNmFUVWItta472j6jXC977+pd5p75kaRpyXaKdsL64pKVjXeQcVVOOqhLfNnhr0HgujMLVHbpdk8+n0C3oOsNydcHl8hI5h/3qFjOnsLWlWay49EJnWorZlFsSGAM/rZAyw9HSrGq6pmVSTbAe8qLk1Vc/jbeW5eUFD5sPcF2FkoaqstCBUV2gtTvP4/NTbt65zfHeLFD/a09jhc4FS7UqQj52F90tFoc4TaFylGScntxHqRXTMqeqjsDfimV5LYPiFR+7BNAdfueui1H8qOgO8GFlQIJSD4sMbWoChJTBQHmJhMWBkrWfVtHbpNb6yCAK9Itd0dP3vbWefP8ivSvAb7GCqXIh9IE4/ffBPGPDAr6icvAp/THEIAgBfHrv8ZmL7xIQYxx8eh9k+8WzJpQpTs9uYmqg0hl4ekYIEVob0hi1+AAwXGCZ267DOY/OY3wUwYMnSqEl4+DgBt/32V/DP/17/xk++OAxP/V3f4aTk8cx4Bq8azi/eMTi8oIb6pBV0+Cs5fziEq0089kM7x3LxTKAIAzOOi7Oz3j04ENevPcyrmlojeHgeJ+9eUFTn/ELv/ALPHr4ENO2vHjnLj/0a76f3/YP/XaWTcfDkzM++OA+7733Hm+//TaPHjzgaH7Aul7TdOFa1vlNf+2QZwKCPM97azr9E5Geum+app/8h8omyzKWyyXHx8fcuXOHu3fv9oWIUu5+WZasVqs+uO4Tn/gEDx484M6dO7z99tucn5/3wXwi0rskhgquaZqtAMQUw5CyCbIs6y13oGcXEmjI85yLi4t+/3Q67QHB0dERr776Kl/96le5ceMGh4eH7O3t9S6H2WzWA4S2bdnb2+PWrVvM5/N+GeQPPviA8/NzHj16xMnJCRcXF6zX696FMp1O2d/fZ29vj7Ozs941MnzGXYGFu1wKY6WbYi2G1xiDgzFQGGcwDBmL9P1p6ZBJhjEcw1LTH7cc36p4fLKG2lGJ5pMv3OW3ffHzvPTCPqI0Yi21rfnqe+/wd7/+Lj/37mNatwEB0dzeUr+KbRWcXBE6nhIdrpvVDGXrT/B7piwCGyoMhtoCFtoOZ5ow+/iwHoF3wcepXLAOI8kLqMBWOI/CIFiKvSM+9b3fx5ufepNbt29RTqboomJdd2S6pswFnQne59g847Casnf7BqJavvzln8LYsCCSsQ7rG3Cars6p7tzm8vFDFqs1qJy8qjDdGQcTwZc3eHBxzvr0lNaBUYKeZjjx2NJRr9dURYmzLe0KzKrGKriUjsxrismEahYCrXCG1ta0jaXIK6zyQUEI6MmUG3dfojw8oll8QH1xTptblGrRBEXfdobz+hxjW2bFHkw81eGcvCwx1kNXI43BdS3adqEMc6ZBHGcP3mHCgpcm+3Rdg1m1SDVlfnibYjYnK6bfrWH6THGEKnsq+v97qry39lVk2AMQsBEAQbCYAcJaENCzSIG/D3ErEhR84gGCLRb8+Xjdp+1FdAAS/NJ93ALSMwoQYh5crKQY4gWSYQF9Ho/3rOsW5y9DvQtiDQ0f/fvpvYnLJlsTCyd5YvaQjgWRYvBczA4Ic17slsiaKa1RWVht0MYiRS4+P6k/CaAhuS209pSTgrarcV2Klcgpyhmf+9wX+J2/43dzdrbkZ372S7z17jdZLhZoAecs6/WK1WoRKoqeC8tFQ900mLYjU0JnLHdeeJFHj08odQldg3ehGuR6teD88QNEFHdff42L8xPe+vp7tE1NkefMp3OyecZkss/f+ls/xeGtY+Y3Dpkd7PPm97zB937+M1jb8ZUvf5n5dI/JdIYoRWsM63XNcrm6cpw9M4YgWc7J2s6yjP39/V7hpWyClIc+jD7/5Cc/ya1bt7h37x5HR0fcv3+fl19+GaVU705IsQgQaga88sorLJfL3qWQZVlvwRtj+pTBoYWb2jpMVUyKMAU6Wmv76w1jCdK5w5oHiQ1JhZcODw/Z398nz3Pu3LmD1prVasXFxQUvvfQSP/MzP8Pl5SUvvfRS/3zvvfceP/mTP8mrr77KarXi5s2b3LhxgwcPHvDWW2/1aYcpe+Lw8LCvZnh5edmvfzBWtkOQkIDNeH+SoYtgV/bAmDEYuh3S/qtSHHddY3j/IcOQfovnITcKjzqseHDWMqXg5sGcu3eOKScZDoXWwrQ84vMH+9Re8c37j1nUGi9hJUPlFdslBIP0AEE2xYiCBSbJjNocSAp6IwZehVrxPSAwHZguMANdCBoMRYc2NQbS1OZilNMQkoUiNCXTw0Puvfkqn/vi57h79wWqcgqomCulyIuCXIdgOhBUIWGp39ZTllP29m9wcvGQVbMKRVhqz2R6yNHxMUYLJ+6crjTk6v9P3H81SbKl6bnYs4SrkBmpS23ZanqmAc7wnAEIgLBjBqPZueMVz4/gDY0XvOQF/w/5C0ADSDOQBDAQ09MzPd29RaldInVIl0vwYvnyjKquvRsGs+ny3dVZlRkZ4eHh7uv73u8VCu87XFPTFpLGeGaTGWlVs2taShwdAq8kSZGQVSOO5gfk6Yi269i1DbmSICxyUtB2a4KhWxaOkFdYPyHNM7QMi4xXkOgUmWeIPCH3W7rOMsoyEjpMvWPX7bA4TL3m5vYFB49/isoKisUho8UCAOkcqzcXbK6u6Oqaqq1wXQdtR7dbcTpKsK3EpXPy2SGTkweMDo5oradtP450Fujn9OFci9HC1gU5GbAHVfm+KJAhOtcHsx4h5LBYCwhchDhmEn2JIHWPXAH9DF5K1de3YWH1LqQFeu96KN31KEN47fBflL7aQGSLO7d/n+grmrwokAq6pgYRHi97IGHIT+qLCNG/f+vC0jygm94jhML3niI+wvr09zAA24/6vEd6gZISpUJyZNt2GO/Da8i0H70IrDd0ncW5kJehZIJA8yc/+Tk//9nPKauaV6/e8ObtG+q6xLqWzlrapqGuarouKCyqdkfbOWwXXB/DCyjy8YTZwVFoSAnqgMBbq1kvb/js00/5+re/oqkqsjRjPp2TpClRZbLcblmVJer6LTpLkYlGqYQsz8myjNXqjvn8kNlsRpHlIa3Tw9Xl2+89z/6gU+FkMuHo6IiiKN7R8t/c3OC9HyR5kWcQ+QVt23J3d8fZ2Rld1w1d+OvXrzk4OEBrzYMHD8jzfOj0l8sl5+fnGGPIsoyTk5Oho9ZaD8XDPhqx34XGEyTaFEcnwDhP3w9VirPwiFTE/Y9OiJG0WBQFP/nJT4YFPUoqx+Mx19fXzGazAaGQUjKbzbDWsl6vUUpxfHw8ECvv7u7w3jOZTKiqavgzGo2G14qw/HK5fGcWvz8y2J/Xvw/p7y+8HyL17S/y+7+7HwS1/3zv/31fybH/9UMIxv5n9LEKAldumBdTzCyFWoTF2AUHPw84oZFSkxc5aZbgRZhvxpuPE32H0ucQDNyAWBKIcCNh+P59LRD9DOKNUvQjA9+HD91bD3d4E8YEwoUY18AduDclEtD/fu862L+CEIosSzl79JgnP/0z8nnG8ekJi/mUNEmx1mE8IFOSJMOYFj2YSRmapsY2DbODE44OjmnKHV0VHApFqnBSMj0Y09RN0E0XAmsNdV2RJ4D1bJZbpjOJ1hJhPMZ1eJWg0gQhA2kyT3NsZ4KvBw7nobWWVIzIEIiqoa0bOmvpkMwP06BC8L2JTe+mZ6zF4XFJikDhnKDpSzKlFa72aCTLu1vq8y2LccE0FaTCYoRAK0nlHaauWV5dUVZrPCHqWHjJncyRp0dkxYjJ/IBiPEMlKVrYXiP/cbbJfILso3e1DgiocRYTR3Lcx2PJd4r3/gwU7y7IQvR6ely/kIbj63wgDSIVic7I0qxXg/TXtLMB5u8XLtsH7ggPxnY9y91iTHvPnXGE857eB0DIIBgQgiTR4dqQmjwfMZ/PydLg7OqsGVwTQ6EisN6j4vu8F/sQnA57QyIxGIjfT+pE4AMEUyePThKKLLjo+s0W33UgQ6x5WK9jURE4aN6BkAnz+YKzswckacqbt696cz2L8y1ppsALhAj3F50lWOspqwape3RVKKQOxUbdVMwPFpRlQ9e1RGfRtmu5uHiDkg5rW7RSCBKcs1gTeE+dtXS9iZhvBGIn8UisDw6kSofsg8u7DWmSkChNqhVaaTab7feeZz9YEESlwHQ6HSR578+G3/fv3+9g3759OzDpnzx5wmw24+LigqIoBqJhlmUYY1iv11xdXZHnOV3XcX5+zm63e2cE4L1/x8Y42hTHRTO6Dr4/3ohb9Dt4v6CIRUy4UN7tiJum4csvvxy4EEmSDJLCaLx0dHTEy5cvBxQlyjI//fRTZrMZSinevn1LVVWkacrh4eHg5zAajXAuZDnE506SBCHEUEC8P9v/Ich+H1X40CL8IcLi+6MBeHdk8aHRwveNKeLP9n/+/v7+Mbe63DFONYu8oAV27Y4XF2/5MjtFqDC37GzDzfKGq9s7qtbipUJ4QbRr8f3YII5dByyzl/n1g8zw6P6mQ19U9FNZBmccF8OIuh4l6PkCpsXbUAR4Z+5zB+7br3BcCdnu0ofXyooRTz59zL/6n/8VR598yXdv3yJlEoiDwjPKU4yXNF2QjXXeYX14rta2tF1NV+3ojMEZj7ASZyUWhUwTjHR0ONabZXhNZzH9mM56T9IKhFW0VY3MJE6D9wqURCcJHkeSajpjaLs2+BggSIREZ5pUpxRJjlYaR7hhCiEpipwi0zgr8FKB7DtABFol7ERPpxQCKVNE4sE48kKSZJJ6XXJ3fcMoyciVwDU7OsJi1KyXVJs1TdviUeg0JRuNSPIJ2WjGaDxFZgWj0ZhEqaCkQNL6j4cQzCc5IhYEKtxznXeYnh8ghpNz7xr3gTTo+8K1b+L7CVeE5MP54HyQCFrnCVw6QZpIijxBqV6l48K5KAl5FeEcCMWp8+Ge19Q1Hk/T1oGj0f9OXNhl5AG4MBZIlerDsnKkVBwujsJzlw1d1wwKhohcGEdPpI8qoXANOh9lkfcFgdwrIr3wQziVABKtSbLgAJh1Gq19H/7UN0lO4Hw4x7RSeCSLwzM+/+wLPvnkEyaTMdvtmmKkaU1CvgvES2MsSIPUgd+gtMShyRA4k+J6lZD3LWW5JuZJ2C4hgAcKrcB5x3azZjTO0IkE4TCmDYiQVjTGBIRGhtEhVvYFkwipif09q2q64T0rKYId9X8vh+Do6GhAAFarFWVZ0jTNYFHcdR1N0zCdTofgon2b4e12y29+8xvyPOfx48eMx2NevHjBbDbj6OiIg4MDpJRDdx7Je2ma8uWXX/Ly5UtWq1UweugXwihhjKS5/bl4HBVEzkBcoOPitNlsfs+Dfz+rYD+AKbohLpdLRqMR0+mUo6Ojd/gTUWnw5MkTnj9/PuxnPCaPHj2irmtubm549eoVo9GI8Xg8LJIx62G1Wg0ZCRGBiIXC/s/2t/dTDPe/HxfkfcLg+0XDh353/2cf+nc8xh8aEbz/2H3S4/e91h9j25ZbhBZMpqDygp3d8ndPvyJJIc9H6DTB0PDN0xd89fQ1VetwMjC1g3RPRH+S+MYCUiB66N+ZMGoldFUC1bshy3cIVaI3NAmJhFFKaPHmXkoYkYPgrBax0v6VXdDRR/jBI9FJxuHpGX/2P/4F//P//n8HKsX9V8Hd7ZKrmxVNBifHU/LRlLbxCGfANuyqhsZ0dNZguo7t3Q3Lq7fcXN9SVx2dhRb60BbLptqxvLtmnGlMFWKEOw8bIOtgPp1QtyXOWrpEIZTGC5BC44wh1ZrNZo0XoAXkSAqdUsyngeyYZWRJBkpRKI1IcybTBbnO6ETwNwjj63A8tJJYIRCpYjQZkaiErtpSWstsNEFay931FVevLhGdYzu5Q0tF5ywqS6FVOAT5fIFUmjTLGU3H5JMZ43SM00CahXh0RDA8qjvW2++fvf5Db6PUIv2934IIuDZW+XC+SdlLUPtzUYgQyCRC1zvAVmFAHnT8ePAC4xyt7YJqgT30qUewrFMDf0D0aIJKEka5DHkSLqToaalIVYYQ0HUK36O5ov/88B4Vyen9zqSpxkvIfYDwi1RSVw3jURIEN8726JjEIQIHou/kwQalTj+iGALxRCjFldaAoOtMQEjkfSERbKxBCoeeZQifIGRAX5wPBlbOBi+DLCuYzhb8+Kd/ymeff8nR0WHww8hAaUfT7SiKhLIqabuS1tS0tsPaMMqQKqRRylThjMeZkKFguo6m9iQaRJHjRALKk2jJOE0p8gylesJ4b13uncU5SeccrfUIFY5bkNSGDAvvfe8suXfvxmNcIE7uf//97QcLgn2IvmmaoTuXUg5z/aurqyEnIE3TQTWwXC5RStF1HZvNhtvbW9I0ZbPZDNB7XLSj4VCSJIPev+u6IdgoShq//vrrgZV/d3dHXdcDfB7n8FJKbm9v3+EzjEajd0YC+4tpWZaDT0GWZXjvmc/nFEUxIAxxhHF+fj54JEgpOTw8HDwH/vE//scsFguapqGua87Ozlgul7x69Ypf/vKXlGXJ559/znq95vnz59zd3TGdTtlut4xGo+F5o/vjJ598wsnJCb/+9a+5urp6R4IZ9z1+/T5534fm/vv/3ldxvL99CBWI24c8DL6vMPghtOKPsd3eXIe5o3CMph6lPK9WFc//3SWTdESaKm53FTfrinVj6VDsp9y6PXQAGLxLpegL0a7pO0iF1Mk9r0AGiDP8qg831D0iITb8PdgT92MB30sO9/ziw004SAwV92RCkaRMj495+KMfcf6jH3Nxt+JoOuLgaMq6blit7ri5vOLyyvGzn/0YKQuUEJSra75+9i2rqkIlCd7D6uoKv13SNR0OQiyut1Rti8JibIXsGhJpqZuW2lhckZKNpujGUglHMymo6y7MqR0hJrlpaOqGphHgYDIZkxYFQiUkkzEkKTtj0CJowYVMkEnOaHqAkmm4efqeXS5C5oCUFu8bkmTM9Egwn06xVcuy3uGkZ1SkdKsVSkm2dxt2q6ZH3SROGc6ePOH44AHnJ0/YVTVlFdxVfVKgkjxo/KVFaEWSZ0g8bW0x3gyJgR9jk6JFCYlAYbpgB6zTBC1lD4f7ni4iw2ihb6mlEETr4n1b7QH/6nkyUiR43Y8WRJDthaf0ILvhXMSDtx5nZJ8ZAODQChIt8JkI3X8RgoKc6zvzfuSglcIag+ytkEPYmB+KaO9KRoVkMsrwJP2+hjInQP/hXHG9VbcUgUwqesTM9YWBIJheiVhTCxWIi94h9pw8hffBq0AE7pjSGufBGAdeMiomnJyc86f/6M85f/iIqmmp6orNbsN2u2S1uWNXbem6FtvHH3vp8NLStjVN3WFai0ShpSZLEvJMU+QpXdtxd3tLInMm4xyvRCBW9qFM1oZ9DUWOD5+LEFgRxgPb7Y40zUgyD6ofTPr+8/b9iJF7BNj1KOV/d0EQXQGj30CcaUepYZzFAwOEH+fov/jFLwZb4sViMRDxtNY8evSI6+trptPpgDZEr4DdbsdkMuGzzz4byHVHR0copXj69CknJyfsdrthEdRaDyz/uG9RJRDfQ8wjiO8lHqC4SK1Wq+H9xM56Op1SFMXAdxiNRnRdx2QyGRCT4+Nj6jrEVl5cXCCEGMYrd3d3fPXVV/y7f/fvuLi4QGvN5eUlk8lkSHyMdsd5nrPZbAbHxkVPgPrJT37Cj370Iw4ODliv1+x2u+HP+/P+79u+b5wAH+Yf7EP973sWxO/vf33/5++THP/Q/v1Db7vtBtS9f/1kOmNUTNgqxde3K7a165nXEiGSuMcDX6APTxvGADjIhIR6g682zIXAdA2N85hkBDLBe4dwHqSGnrmNCyOskD1AwD5NsHalHxWIPtSI/kblfbwJ+977ABAKnUhOHz/i+PEnyHHBf/nl3/HV18+ZLeaMDhcU4wnF4SlNa3h9+QJvf83BbIZXgqu3V6xvl5RlRdeGa0VIz5OHDzk4mPPN82+orq7odkF6mU8S0DXFQYJsBTJJoQ+CkbsVeVIQjWKUVCjjoAk3R2s91BlK5gjd4gV0GEyaclxMuHhzideaYrwgSyekowlZnpEnCdJ6BB0pDa2TeDTOC9pmx8WrZxgPSZ6xu35F3XZU1uNVRrvakjrP4aQgy0fo4oDOBS37yfkxxw8fULUekeQcqIIsqai7AHNXbcVsNmY6OcCnRUgN9AapBDrNWZwcf4QzOGw6PUeJYF2sk1AACNUXhwTEyjl/b9hG6G6979HjfuQ6qBIArfQ9miB6l1Ip+yLCo4S6J24LMXgZKKV76NkHRAsYkIV4XxD9/wkZyHL9aE0JGZQdKox2lewdZwWEYKG4zzH62N0XDP2up0lO1/OApPcowr56GbQ2DvDWooUkkRJJQBhabzFROYEf9sN7jzctKklA6ZCEqlMW82M+++QLDg8Oud3d8O3Tv2W5WrEpSza7LevNmnK7oy4NTa/OabqOujN0PS8IH1IUlU5QQmKBbWuwdSgwhZYkypMk0BobfBJwGGUwdOAV/awLJyROKLwKIzSHpWlLvEj7f8NQuBGOoaJHF7zvJ5Y/fC/+wYJgPp9TluUQUJSm6aDz77qO5XLJer0eRglt2w4Jf59++ik//vGPqeua+XxOkiSUZcnBwQGvX7/m8vKSTz/9lLu7O4DBlGiz2XBycjIUIdGFMM9zvvjiC968ecN2u+WTTz5htVqRZRl/8id/wtOnT3n06BG//OUvAQZ0YN9SN6IDsYiJ85v4+K7rmE6ng1XybDbj9PSUL774gqOjo2GkcHh4OKAOgc0Zxinj8RhrLVdXV/zmN7/ht7/9LWVZ8qMf/WgodNq2ZblcMp/Pefz4MdfX17x9+/b3OA//9t/+W/76r/+as7Mzjo6OGI/Hg8ojHusPqRDe3z7U4e9vH1r04d2kR+AdJ8UP8QY+RFTc//6HCoU/xvb5z/+EixevuL64pilrTGOYzDrG4xlPDhUXW8+u9HRdcGhz9ETCSBzsb2wBaRU9f7DjgdL8H/6X/4VPPn3Eplryy1//Pf/ur37F27bFExIxEb37YHSUi7Cf7UKH48MNLSz8vYXsUEDE6GI39HQIQZaN+PJPf8rhgyN0nmKdwTlBooNDoG09tatRUjOZn4GF12++gdaQZhmnp4958umPgyFTf+Ntt2uUgqasGOcZ0yLF+JTOCQ4PC7a7krIVSKtwOqG1HW3nGBUTrJKMXcG8S6CQHEzGKATPr67YSUM3keTZGNcmKJ0wmc6Yzg4pxJjjByNEnnB8fEo+mSJUirOepjEUOsxxjU/wWISwCAfaC2bzQ7bLO9zdNZ3ZoZ0iqx2iaRhPFCJJqFtL1Za02y3Z5ICjR58wPX9E1XoSlaN0RpYl4SaqghnM4eECLYM2XrThfLcYRKqYH8xp6+ajnMMAn37xI5RQ4XwidMXIKD+0CCyqnw/7vvtFgJK6n8P7vcW6Dy2SEmts8Mt4ZyHprW65v6bD0ix60pwKxZ8QwZ9BmF7aHpoU60JygVRxXCuG51ZCBN2/VnjX84ti90osTOCeOxMWMuvBOINwHo1llCZkSpIriZYSJyVlVfPo9AGjJA3jEkBLgVbBbFxpTZqFQq+sW9ZlxbLccbtbcm1uaVpJkk558PAzzh88JisKbld3/O5vv+J2dctmu2K729BWJabr6JqO2+WKugkky1RLLDJ08AIUwQVTyCQ4HHpCwS9AKkUiXAhpwqETResszvTnnYTOhiJPSo1OUqTUASHoj4nUGpyhM22f6BiOsRQSKSTCOQwMaKMHXN/Uf9/2gwXBbrfj8ePHzGYzgHdsi9fr9bBIFUURnJe2W7qu4+rqiv/6X/8rf/7nf85isRiIiXHBu7i4YDab8d1332GMYTQasVqt+Pbbb/kn/+SfUJblsNifn5/jvef29pY/+7M/4/r6epABRnj+9evXA2dhf8QQZ95RORCh+bZth7l8rID3eQlKKc7OzvjFL37BgwcPBj5AlCNGDkEkFV5dXQ3Fxu3tbTCFuL4eEJGmafjiiy8wxgw8BiEEv/zlL4fjFUcui8WCBw8eDGjLb37zG5bLJYeHh4ONc3wP+4TKuP2QAdCHlADf95h9MuP+c++TBP9QsbG/vV9g/LG2P//f/ivefPUtu+UqQHnCYdqGcr0OuvZckgnJroS6c3TOYZF4kSDouwv6kkAECC83jv/pn/5zvvj8x8yPpjzKH3J2es5ifsT/4//5/2aNwmgB3oYOx/V/eiVBCCgyiN6OWLh+jBCrdx8gTdsjFfjAyM7HM/7yn/0lJ19+RjZKKbdrtustTqRYabDCkghPVhQkSYbNchBQJIIJK8ZpiiwKnNZ0zlN3HV1VM00TwFB58HkCmUaZnFROe5+ECmscbW2xtsNJgS4yXCpoEByoGV403NV3bNoNqR5RS0XZNsgkATR5keLp2JgdXQUmc6BTRlmBsxZT7dC6RQqFM4aq61BCkySKttrRdkHKtVqvKasd0jgwJV25QxNy3qVSqOKMctuisjmLT5+wODwjL6aoNCcpMrIkZ7PcUjYV47MZx/MpwguSRJFqTaIUXV3TdQ3YLkC1PqVtWnar3Uc5hwFevPhrpOg7+phbIfcB9XunStgv0FVArCCeyAT4PTzWORe4HtFowAcOQiT/haZKDGu6J5IS6Tv43tNf3BNoYwES5ZFRJhdHGLJ/At+/Vl+G38+9+xm/J0QzKy/IpeZgVPB4ccRcSGTTQFXh6xpvDU6C6Qz56k3v3+XxEioVjpFEU5UVuGDrm41G5LMpnx7M+GxxzFo/5OnFLbOTRyidc3l5w+3dDavNLcvtkt22Ztc0tKbB2g5vAm/CywyfNLjW0roGL9U9CVZIvNeEys2E4y56LMWFVNNECZSHsqqoW0NrQ+NgDJAlPDw749MnnzCezNBpRt0ZblZ3pFlGVZdcX16wXN9hTBcaDi9w3gwoo7PhPAmcUw/OhyLue7YfLAhitG+c5UckYDweo5RitVoNPAOl1OAuWJblMOMXQgwSxCj1K8uSqqq4vr7mwYMHeO8HWB2CZbJSii+++GJAFv7Tf/pPfPvttxwfH3Nzc8Pr16/Z7Xa0bTvYAa9Wq2HeP51OB/Z+NCHSWg+jj3iyW2sDy7M3KUrTdLAd/su//EvSNOXk5ISiKIZsh32lRYw6jgu23avAsiwb1BSj0YiXL18OCIcQYpBTjsfj4Zh0Xcdut2M8HvOzn/2M+XzO69ev8f4+RTKqH2KB9b7KY3/7vn/vf91/zPvFxfuqi/dVDPHP+yOG99GLHypU/iG3k4dfMpue0faMaGMadqtrLp59y263RiWaTCT4RCOBynhaB947EDFQJszuAlcFJlLz+MEDVJqCTFE64/Q45X/1px1/9/VX/NWzuzC/dKKHS8NNMki2ejJh7zEg+mKBHiWIxUCYf/aGPFozmk958NmXPPz8CSefPMJrib7RYB2tsbSmpq42QTWThPAXqTV6NMJUIyQtQngSrSDVmM5gWoeXkrpp8bala1vytGA+PkSJGmMtm80tTUNQDeTgGhNkmd5T1yWjZEbnaxIFPkvZWUfVtTgvkSoBL/Dak2QSYRKMc5RNxc5UpCKlYotSC6Qf45pkmG5779DCY7Ydtzc3bMuSbVlS7zYcaMnWONrEkAsZ4prTJHgUFBMWByNOP/2E6eEpWT5F+J5slmrSfERnPd2qDeE9SqN0sPpt6oqb1ZosSei6hmKUUaRpuMlbw8G4+CjnMIBwFcH6KhQEYQ7eowHEsKJAmIvolvAeP9hqxtFA/3zxib1H9imFA28Fcf8c8v6x4TH9tex7YqP3iCErJHAOIgcn0AAFIdI73gvCGCGiXpFsGIORECIYcQkFJAivKKTmSCkOdi1cPee6LDFNg+tasF0oSkQAyFUyYnZyzmhxRDKdoooCkaRonSE3a5rtDukdHZ6ucWxuN3TLHWpxxtnpI95sNixXr9ms1+x2K6pqw7beUbaO1nk6ZwLh0ICwEoEhoUGnBiUEBkFHIFsqD9I7vAOr3DDCCbklFuU9ynlMZ1hvS6yXqDQjz0PjOs5T5gdHjMdTDucHHJ2cMJ7NaIylaYP67vnzp3z9zVe8uX7DZruia7tghuRcz5OwIU29Pz/2QjE/uP1gQXB4eIj3nrquh9FAJPHtRxPHG3+E5/M8p65rlsvlMFNP05S2bYdZeHxslmXDorZPrEvTdCD57XY7iqIgTVPOz8/59ttvWS6Xw0ghKh+i78BkMuH8/HwwD4rz9phtEBesuA+xe41IQVxoo+wySi7run6H7Bg7dWCQRcYCaTqdAmHsopTi5cuXvH37lqZpBinmfD4fXjMWWxH1iO6G4/GY09NTNpvNgMocHR3Rtu1gwfw+SvCHtg/JB+O2L0H80O+9//X9RX+/ENj/+8caGah0zPggp+iJTN5bqtkcjeZ3f/1faZsarzokmkQE3b13IriZiQQvFa6XLMVGJ9GKrEjxMmYJCNIi5/zshJ9+/oj/8vQG20OfLsKE0ZHQ2T6TwN2bDu3HGON6h0OBThLyUcFkPmd6fMjJ44d0SrLebZnM5mR5TlFkqDqMQkxV0glF09vOJnmBVxKvNZ1J8coiZZj7Sm9RHjpjqTpDIqFpanbbLU1dYbqW1nQ9HCmBENeaihxjWlpbY1zQlWcjgXEG0zmqtsN3NZredKZI0BqUhHExwjvHtt1RdcEMppUJVb0j8RLpO6yxOCyJVnRtxXa9Znm7omwaOmtQLsSpd01H4z15VuB1ikslItf4XLA4f8BkcY5Oc5IsIdXBzKVzYe6eJJpCp0jvw8jGWtq2wbZBCrozBkkYkyUQzovOflTHTe3tgAXEpVT4CBL74YY/oFkw8E+GDl32ZLM4DiM0j7IvBgLvIPxqeEh/HUciwvCD2HVH8mLo9MXeVKJvSe9Bif63Qs0RY44jSuDv92uQRwpM66i2W3atweExjUFtd+zaEusswtpAlRQgUMhsRL4YM1mcoR9/Sn5whCzGoFOU0ky6lrSqgnrCW6wzNKbBNg2Ng9vrS767vmK5WVOVJW1d0XR1kOdaH7IRvMNYgbcS6QSZaDkYecaJQYqUXSvYdoJmQFwM3odgKs994S9duAd0XUtVNdSVJckKsiQjzwocgs5AXXeU5Y5Rqql3GaMi53hxyHS+IM/HTEcTTGtBCtIkZbvd0LVNb5QUUAPX8wcg3JCE/++UHUZb37quh440KgK6rnsnRTASDSM5cLlccnNzM4wLot/A9fU1dV2/M2oYjUaMRiPatuXly5dorZlOp4P98Wq1YjKZ8ODBA66urphOp78nx9vnBmitB7Qizr4jGhAXpgjbD1KV/ve990POwHa75fHjx2w2myEKOToY7nY7lFKD42IsGIwxQ6phURRMp1Pevn3LixcvsNa+kwK5nwVxcHAwvMZms+Hm5oanT58O/gdVVbFeB93q559/TkxijNbOcZwxXI794rxPPBzYpnvGRvs/e/9348/i1/3HfYiE+KHn+9BY44+5eXePXigEUqeo6QL9qebi2Wvurt7SmhIvaoRI0D4l9YLO9zGpSgdyYB9oBAIjYddUCNkznGU4n0ZFytFi2lsYB5mg7y98PxQBIeI4MHz6YqAn/fTiOoSS5HnB7PCAw9MTjk6OGc3m6GJMZTzV5TXOOhLpSROF6BStJCyydU0jRIj69RaVZP1MPWUseiY0BJgcqFwYk8geXjSdxVqHtQZjG5QC7xXCKmbjQ3COXbmh29mw6CvNZDLmZnVNV5WYpgvKCSyTTFNkIXFurAsOJzMUkK0FW2MwrUA6Tdt0lK5CWrBth3cN2TjH7Dbc3dxSVi1IRaYlEk2XgMoShGmRMkGlI5JRRjKWuESQTMcg06BNl5I8z1AqoW4arPDkqUbkKVIIdL9IWuvQSpEfHLBcr8iTBHC0TUtXWTbLNTc93+ljbBKNRA8kPbxHON/LCuP8v7/GfCQPRh5An11go4z23es7BAC6oXC4L+wZFnB6Xo2H+8Ulugd6Hxby/WKA/tpDDPbCcd8CcdD1+xWKEdGPE4Jat1dTtI672w317Zq7rmNhLInt6HRPKAdUvy/OO5Q3jJ2gkBIrFYVUKKERaJRPyMYT5JTAL5AgnaGrS9rlDVeXr/nqxdesyhBM1LUtbWeCP4fwCN+Pyzw4L/FOIhwI0ZFLGPUT0UY4pLe98igYOTkENnbo3iN70rHpHG1ds6tqWgNKZ+ACqmOcxXnPZrNhlIKwNU2zY7PdcFrXzGZzRkXB2ek5Dx88oukapFRkSUbbNqxWt9TNjs60oRlhKCPvwZoPbD9YEHh/7/AXCW1Rnx8JdnEssC/9i14Ay+Xy93IEInwfkxFHo9E7SoZ//+//Paenp5yfn3N6ejqQA6M2/9WrV4MCIHbscZGLngPR5CgmDL4f/rNvlBMX1jzP3+lwV6sVL1684JNPPqGu60FdEMcjWZYNKor5fM5kMuH29hatNefn54MlsZSS6+trlFI8evRoiEuO6oeqqt4ZV0ynUw4PDzk+Pub58+e8efMG7/07RdV4POaLL77g9vb291CP9zv87+MBxPHGH1IovK/I+CH04PuIh/FnH2PTOJSSOBTO+T7cQ0Ge89mf/Zzkd5o3b5/TtLtQwQuHcALlPNpajNGgUtAJOI3zmlJ6nr55w6NPHoAKBCyJo20b7jarQJYmEKWc67PW96yKsb3fwF4SXLyZKikZTcY8fPKYh19+ztGDM6aTCZnO2bUdTd1Sbu64evuGItOMEoWSkjzX1G0ozF1bYaSn854096Q6oyOhcR1UFUoJHBKcIk9TurqibGvybMT56afM66YnUN2y2y5pvENnOUfHx2zbkq2vsDVgBIvpjEzntE2Hqmpy5/FKh/TB1pF1joPJhPn4iMl4isSTUVDUhg0NDQ22M+yaNb410LQo1yFkQdo2tF2DyjTjgxlSCu5ub6iEZ3o8x1wvSZMRk+mC+ckh2STnarPBKEcxUaS6YJxP0DJBeE+uNQZLMhuxMS2ND/e1VCfkWU6qE3Si8MKTCkdV7lgtV1xdrnn+8g1vl8uPcg4DpNkCKdMwggEisTCmF3pvhy5/P5Mgop+uH/7fk2VFkO+Z0CkLAldAykBIU4PVuRw6/4iHDW5+QvRmQP0II8ySoH9cuOYDCVFI1aMJYeF3ziHkvXPg8H4EPZdAIrxjMgHfSuqy4sKVKOsQNigeJP3jfSA82rJBX19z8eu/ZXpxwfjgkNFkRj6aUKRz5gcHJGmCEgKtFJ013K2XPH/1ghcXz9hVG+qq6tc8h3M98uI9mr6rj7xiHEIEL4TlBmqpMLal9orGS4wXwWjMWhAa2+sfJb1xn/XY1lBWDXUbQofK7RprLSNjSYvgkdJ1Dbudx5kd23LN9d0tL1+/4vL2ml/84i8oxlMODg852m1o2y4QT3EUaR4kkeWWpgnjv0gA8fz+/Ttuf9CHIDr5CSEGn4BI3Nu/0SdJwmg04vPPPydJksF74ObmhocPH3JwcECSJANMHhf15XJJWZbMZrMhDtk5x+3tLZPJhKIoUErx5s0bfvOb33B5eTl09rFIeX9mvd1uefr06VAoRIOi/YV/PwMhFjfRKCjKBv/6r/+ao6Mj/uIv/oKyLHubypD2eHd3x/PnzwfZ4eHh4eBEGPMXIrnxu+++45NPPhneu7WW29tbvv3226GgisXAfD7n4OCAyWTCz372M16+fMn19TXb7XbYt6+//pq/+Iu/GCKU9xMF3+/i4/Y+ArBfQHzIrwB4B11439J4v/h4f5TwQ/kKf+xNJ/fnR2BmhxuhB04++YLDk2Pkf01Z39xRNyVls6Nut4hWolwdqnWvcTqDpECmBZWH//Cbv+dkNmXy53/KeCzZlTXfPv2a/9+vfkNbBNTMdw7fGlzX4Ux/c7AhwTCMBnyP1wZqlVaCxWLGkx99yvH5Q4rJFGtaVpslk9EMKRWJ7tDa8fblBdYYFgdTzk+OyFJJoRNa43Gqgwak98G2uEgh0dTGo9NgtNN0jsYJhNAI0zIa5+gkJ8vHWOs5qCvK7Zq7q7e8vXrDZDxjvb3GquBgN8pyXFKASnl9c8W2ajman9G4jrWvUfOc0XSCagSzYsxkNCbNEuquY4vAJhlZAaau8SZYsrqqw9QtHsvlHcwkFALG85yzBwd4JWjMhre3G/JRx+xoxMH0gCQd0zaeVrQkozmPDz9ld7tBLCQ2z5DOI6wPAUHO4kxAUKQQeOmRWcJIp8EPAUtXVtzdLnn1+pqXF3dclS1uPKV48icf7Tw+e/wTlEzD5yVijLe4nwf37H4Xba0FA/vP0xP5pBjm9r5H75VUCFyIBI+8AQJTPY4bpIw/C0qc+JJSqAEBCPeB8NpSqfsx9XB/gbgUxZpEChmKAlSPfsTMhDCy9S50+MZZbjc3fPPsd9y9eYW/24RZuexROyEQSocCRyVUTmM3Dbt2RbZuKUYlk9mO23W4ZpQQTCZjyrri+evveHt3RdnVIUfE9rbmvUxSeosWngxDkob3oAjonFYgSFlvoKxE4PIIRycExrtASvU+cD8E4XlFGBt4C13naazD+MCcsbalrYNEFilwpsE1Ha6TpEl4nw4JKuHVzSUv377l53/6CxbzAx48OEPgcCYg90cHh1xdX3Fze816u6brWqQQFNmIyWj6vefZDxYEcYuWwLPZbJANRu1+LAQODg44Pz8f3PyqquJv//Zv6bqO6+vrwe0vzsiBIcBnt9vx5s0bkiTh5OSEw8ND6rrm+PiYpmm4vLzk9vaW1Wo1BB9570NoQ1GwXq8HYyOlFHd3d4PBT57nQEAnIg9BKTUQBd++fTvkBkRPg+iN8OzZM/71v/7XjMdjZrMZl5eXgwVxdCq8u7sbVAu73W7o4GMS5NOnTwdyYUxqjGqEyCXouo66rrm8vBwSDx89esR4PObx48fD42NAU0Rdzs/Puby8pOu6d0Kivo8fsM/3iCjB95ER76/ne0Th/a7//UTG+Prvkxz/W+SR/2Bb341EFq70IVs97KOl8XD6+FMePvkErWUg0t0t+eq3f8/V5Wtc1UDre7qyxuqMbZ5jqhn/93/z/+Jvfvd3HM9zdk3Jt29veFM6Ou8RuxrflmEm3hm8MaHDsDYUAF4CkkQJRpliPitYnB+TnBxx9OQh9bamKxvSJCUrNDebDaZt2G2D1Hd5uyGRCVma0TQtYy2YjFO2O0PrAyJhTIdrKiwJiVBUxlB4yyTXpJkgswG+NThSpSnyUWBH40iyjLE8YDqd8+kXP6JtSr59/g13qw2usRRpSj5KWJodlbJ0QFmHeatVBm0Syrpm21lOpaNyFatlRVN1ISRIwuxgSlmtmWZTbGJp0g6ftbTbktumY2s6sJ7dxYZt6yERbK43sK6YP3zCuJhQVVBMM5RO2KxKynXNs+w54+kc1Vl2VUWe5SgkbVnh24ZsNGJ+eAYkuL4YM8ay2Vxzc3nN+q7k6ZsVl62kPnhAcV6QCMVHArkA0CJDkgKqX5x9CPnpa0qkApmg4zU6NAbvkgn7oONwXRMWeEFw9PM9rB2nBHjfqxQ8rudbCfaNbsLTKyGRKrgfip444/rIYe/7mbXoiwm/N9bsTXgCebcf7eJQgzOYRmKRwjMtpvz5n/2vUf/of8CbwPWw3vb7H7wRpJSkSYaWeu/eFl4vMFMIxkvA5eU1L1+95cXL19RtDbQITQx46L0LLArLyTTjIHV429E1hs5YbM8jMLbEdIbKqsBRMoHp74XGC40UQU0gvO95IOGas07RmD5npEd1BB7rGuoq5EKAJ1OWJgetg2mSlBqvNOvthss3b3j+9Cs+e/yE+WRCU1dsl7fslKTtDQFN1XBycMDp+WMePnrCyfEJqU74vu0HC4K4EMQuOyoNhAhJhNGK9+DgYJAXpmmK937osu/u7ri5uWE8Hg/xxScnJywWC1ar1aA+mEwmGGMoy5Kjo6PBNnm32w1+B9Eo6NWrV+8s3nEfgYGMWNf1O06DsTj4y7/8S77++msODw85PT0dIPv99MO2bVFKDZbMz549I89zjDEDihHJgOfn5wOqEQmNkUNxdXXFarXi5z//OVmWDf4NwPA7kZ+htR5GIJeXl8xmM0aj0TuOiPGxUkru7u54/PgxV1dXWGvZbDbDc++bRQHvFAHvowH7C/f3jQU+xBeIKMv+GOZDVsX7RcPH2OLYSAiBdR7TdRjr0UlwLlvvSlAZ4yIjS8MCMZkfMz8/ZXt1xZvnz3n+zTes7u6CBWorEHXBrtzR5hm3u0tUnoPUeA+2dfi2w3YtztZgLMJ6MEEa54RDSZgVYw5Pjjk+PeLwbMH0bM709Jjp/Ij1csPzr57SujY4GnaGRCcoNWY8TXGyIMnndHVF07UsV0smkxNSrckzhTdgvQwUBSxOtIi8INEJ40yjaWm7FoxF6YT5bARJgvM+OK714w7rLFqnKK8hkxTFhNVqhTUNnWlYrm/xEwVG47WglhatNIWRtMsGYQxSWZ599ztUMaPzCa6FxMCkSHnyaI6cpdwtb7ktt7TekyY5xXyEFgl3L1+FGW0i2e5WdD747CeTMS6dks9PKd2G2jco1+Jp0Z1nt75CFykTJgg8bdfiXDB2UdkIp1KwhnqzpCpr6ral6SxeJaTFMW1Tsfj8mEOlUVIjpULImDj4cbbxdIaIEjYhECKS1AZy/v22X4i7YEYTrtdQGAyjARnSDKVSCBmjBfsF3wWFgHNmMLbBMyhmrIsZAr37prNhhNCjBTFwORQLIXhI9AZEYV2Rw74S1Q0C6EmCzvWue4hBxSAEwdlQpr2RVyxuekI7EqWSHqEI5L1QnLjAm/CBfNh2huXdmqbpmIyndLZPv+0MToQiS3jfc35a6rLiO+uDMgiPgeAFAAjhSSRMc92r+zzG9MdIKKyHVIAexjke7zzGejoXfAYiibIXauC9w3QdWgm8tDgrMM5iO4sXHQ4ZHBuFoq1ryvUaKQVt19K0DTJ4MhPcFyQyDetRU7d8/dvfUlUV/5f/8//xg+fZHywIkiQZbvZxJh6d+6y1LBYLFosFT5484fHjxwB8/fXXKKV4+PDhcGJuNhuyLBtkdsYYZrPZsNBG2WCcvVtr+c1vfjPo7YuiGNIF1+v1gDZ0XTe4CJ6dnfH27Vu8v09d9N4Pi3EkKiql+O6773j79i1lWVKWJUVRUJYlSZLQdR0nJyd8+eWXg9nSbDajrmsWiwWHh4eMx+PBEyAaEkVZZdM0w3P9+Mc/Ho7lvm3ygwcP2G63PHr0iNevX7+jNohSSu894/EYYJBRxuMWVRtRgVBV1UAwvL8v/H73H19/n3AY//2hsUJ8nv3n3I9S3h+/xN/70O98LFJhVK9IEeJiTU8UaruOpqmptzXj0QgrFV0/K5VIRjqD+ZTZ8SHj5Q3JKEN6KLdbyvWWdrfEmBTTKYROkTJFS42TIWAkBKhH5CS4jynhmWSag6M5j7/8jPPPHjM9OiQtcqQSFMWILM2p7l4zUhkqlT0BUKHQVG1NawzOC9J8RJGN8G0TcgRccHZTSqCMw7p+1iwEtrPY3OOVQiQeLVOEVoi2ozNBviSzjNaG2arrOqztCMmKQT9dtQ15MSfP79hWGzpr8MZB7aikIck1wkk0ApE4nG2CO1xn6dyOTFhSVTBKJoyLCUIndB2kTuJrg/Y9zOwEPvg+U0ynaFGQJY7bcsuuteTJFOcMq92aIk2wdUsnHCbRdE7QekmuFFIollc3mHHBeDojK0Z4LfHWsdttWV8HG3Z0gs7GTGZTkrxAJxnT49BJKoJrn5ASpETycc5hgKo1fbbGQNkb/jjuEw5Fr1AJUH9vx+sDDB1SC2P0rxrcC6UMsP+HCMPSB3JlQAT6QgOQKhIKbb+oRWJjWNDoF76oibDc2wh7PG3vGIuPlkeBfyCHN9h36jKMFSRqKCjiCFyqwHeI3wtfVG/QBAProS9mrHdIoahvb9msNqyXd9Qm5PEgoCorvLB9jSJ69UMo5JVXQ8hocAcOUmQpPN46aluDEEGN4FXgLBFULA6PFX0B44NzYOfEgLAEs6eYjdqjLz3/QmGxUvSW0mHMGPI+wyjH2xZjVyitUVqh0xytFV3bYayj7Rqq+pLLy2vw0LU/bMH9gwXBPhN/P/RHa83BwQFCiIEEd3JywqNHj9Bas1qtEELwL//lv+Rv//Zvefny5WD3WxQFh4eHw/NGXkL0DpBSDshBPEEj8W+z2XB9fU1MNtz3QJBSDujBfvca/x6lgl9//fWwgO7r+OP7jc8deQDGGG5ubob5fZRcxufXWg8BUBEBiPsVcx0uLy+pquod3kMkaE6n06G730cqohwzujTGogcYcg4iijGbzVitVu+gIvsX9v62v/B/H5T/Pplwn3PwIY7C+wTGDxEPP5YxUVEUvRsafZKgo+sMXdtye32HM8EUxblA7IldTtdUvPnuJevlmizNOVqccLBYIIRgu7xjc7Xk8u6OumtCZnpXB4mvDHN5iUN4SKRHJRqtFYlWTMY5n//pjzj58lOK6ZgsH6FU0rskhmPWNDVaQYen6QyNMT1z3+G9RUsVOh4HUqcICdaJIO9T4UbmnQ0Lq3RYDMr4oDawNVoIpNJI7bFdh5KBF5MoifWeDoczQQGjRWBId8aCkOg0DZr/LkEmEryhyBSZyJFWoKzD2xbpDUYotOuji21wlEtFMHvqrGV9u2Sya0i9ZJpPkXmOEIq6qnG2ZTxK0SLDuI50pPE5IcvetHS7ko1zWKOwiUKlKbLIWGQF00lGkWpMC3XdoHWNFj1xueuwxqFVQjadovKcJB2RpiN0kiCk6BcZGXzy+8UVofr59sfZhNIIkQTSWA8z35Px7uH6eP7GgiBaEsuewBcKHEFIAgyLZN+Ihw7WRTLs/XOLPtsgFiDh72FMAArZpwJCXJTvC4Ow5sfut78/CDEoEKK6JvgqRH5B5EaIYZ/pxxWRLOm879MIQ1Hh+sVWqbB4xmCy6I8QVEY+qGtUgjUd282GbV2ya0qEFBjbITCBX+HjOwnEQd0nK4ZiQfajhzDTd7HYkeFaMaIv5AHpQjppUCqEfbcOjHVh//v3Gd5fj6r6wKNwTuKEo+vC5+pFsJ9yPjifSxnI6J11CG1CoSdDKFvXdngIceEDCtP/sn/33ry//UGEIKIEceGLXX5cICO/oGmaAdaP3gMPHz4cOtxvvvmGsizfWXyj2iDKBOMif3V1NUDl1tphZNA0DTc3N0OXGkcYMZjo7u5usDreRzXifLuqKt68eTOQJOMWF99oUJRlGdZaLi4uBt1/0zSDgiLKBSNZMUmSYTGOPgVaaz7//HNms9lghjQwfp0biiClFIvFYihs4shlsVgMxyoWEKPRiMlkwpdffjnsR5qmHBwcDFHREVl4vxh4f4H+PjXAh7YPEQffHzXsFxAfk0T4/vbufjmcN1jTUm63rFcbpuOi7wQc3hvapqLZbVku71heXZNlI85OHzA/WLA4OmI0HlGu7rg+uGB8e0BVVjR1Q92EBEHvHIlK6ZoWvMFZg040aZ6h0pSjh6c8+dmPKQ5nPYOZAL9KEWJgpWByOKO6XeJ2hrapgwTSJGRp0iMYDDdoj0AnGZ2BTAeVghQEwyNh8TbYqZrOQppSthUJAea01mNNCGLRSqFlsHvt8MGjwHpab0IsrAoscakUOslIU0+qQQmHyjWu6+fZ3uOcxAqHlzDyCSrJsVqiZB83bQLxqiw7CqGZHZ+THh6RT2d447i9eMtud8e4SPCdZVe3FFgSLM52pIzZrTdsd9cIPWI8nZKkKflownw8IS8ECRInoOlaqqpGeBUWDqHQ+YhiPCXLx6gkaNSFVPeQuu/h1j5QKWjse2j8I21K615CpwaEQPSde+8b3HMIo/3v/feI7H1xL0aM8/q+RQXoWfVuKAoEUf0SFyzCMRm4BIGQ6MW7BUGE/fH9CDH+LpHoLoeu3ftYeIhh38PYgX5PRf+a9/cU53o4/53N318TzoFS3L8qfZEQFA1FngXlTpayKdcIfFhAfQcYGFIDxVAs8U4x2I8j+v1zXuC8HozEXI+CSG+RzvbHPRztYRJhfS9FjoVSHJnQh5uFcsP2yIvokRDf2yJbG4KapAyIQ7Qpll4gemRS9uFTAh8IzH2xtX9c3t9+sCCI89f9WX0sDiB8mF3Xsd1uB1fAxWLBwcEBjx49GvTzz54949mzZ8NsO/r613U9LO5pmg7duBCC4+NjptMpVVUNZMIoQYySxtgNR27Cixcv8N4P+QSxYInOgPG19t/DvndBdDKM3f9msyHPc87OzgaDo321RZqmjEajYRwQ5ZMx2viLL75Aa83V1RVVL2fZH73EUKNohxytn5VS76RBxv0rioKjoyPOz8+5uLjoL44wCjk5ORnkjNGg6X00IH5m7y/YH1rsP7TtEwi/73vvP/9+wfAxtrqp0UL2XY/F9AXb6m6Jsybo+HE4B12zo7y94OblS5bLHbOTM5589jnFZIqWCq0lqQQjJWmR8/CzTxEOTFdTtRV12+DqlkwKVss1Xb1ms2nw3pJqgR4VHH36CfPzs2Bl7D1tZzC2QxJmrS2O008e87pqsHZJ17SBpCj6rs6HzzxJNY4o4RV0nQsdtBQ9ZNo7IzqBFzKQKsWYuhOkvsMKizVdiGJFkBbguw6dCAoJTitMa2g7g0xSsjSlbRvytGCUz0AEHf8kSfBOcLu7wHUOgUb7FJQlVY7MJSiV0uk8yMMcoCQqHZNPF+TTKfPTUw5OT8mKEaasmY3nXF69pMgl5WaLSDt0s6OzJTJPGCczfNmxXN4iEs/cOSYqIVP9YukVpvV0jUEojfWS1glGxZh8eoAejdE6EC0FYuiaB7TaB/TF4/AmMPedMbiuBr78KOdxkPS5HnKP3WQP0/dZGQiPiwXB0BkrECEyer9YkCrICaVW/fXad+U+dMKD26Bgz1I4LKpBmhjJhdHnhWFuHwmHAanqx5Nxfu7D80FY+OJXQbjXyRi4RITQGd6r6D0jZL9Ae3zPpwAdtL5DQRJUE0Nf3D82HMesyHj46AHbaoP1Bl3t2O3CqNY7Hwf5A/rgPZi+shIhkIDBHVGIHiGIEdH3x0H04wEpGd678yEBMnAkeHdp9vfFWbxdDsFrMqQ8Sq1DIJOwPYgiY9k0yEClCp+TSsIaZ1wXsg5ctDL+7ywIXr9+PeQYCCGGBTPC4hECj/PsJEl48OABn376KUmScHFxwe9+9zsuLi4Ga2Mp5TuBSHVdkyQJi8WC2WzGdDrl4OAAYwy73W7gCWy3W968ecN8PkcIMXTxsSO/u7vj8PBwkOftdrshrjlC78AAx0fyH8DLly85PT3l7u5u2JfIEZjNZoOCYT+COKIEcTyyXq+HQiPLMj755BOOj4/x3nN8fDzwAqLk8smTJ0NK4t/8zd8wmUwG/kBc2J1zA6chz/NhrLBcLof3EW2eR6MRRRGsVWOhtF8MfKgwGM7DDyzgcXu/kHhf4vh9iMGHfvYxNtd33950WGuwpqM1Lavtjul4RpYK2nrNerdifX3F9uaGRGhOHz3i5MFjismMJMtxztC0Laat2DUN1gvaXmYqdMI8H3GWpdjtkpvLV4wLyaYNzODOe2SWcf7oMz59/GOkS0lzTdlsA0fMC0xnMV1FYxzT6QyHpu0cTdNCokmSHK1Tql2JkAprBc6C6QK9qUuTYIWsNVJrRBfgyhCO4nBdh5ES23nsdkUuO7JEo4QK/v1ViVMaKQSjRAeEoHZ4oaitRVjBfDbhYDxhs9txtbxlt90wSlNMa3GdpS47nO1Q2pONPWkG613HKBPYriUhI8kLJvMZ+cljvvjZL3j04JxEapqqousNy2aLBdbUdN2aLJEIPaUYj3GmxrqWRBc8/HzMtqyw3uJNg9vcUW9WrLRmevKAYpSRjcbk0zn5ZEZWjEl1jtJJT7gKdhEqxiqLXpJnHG3XYhpD05Q0dRnCbOoS15XA//hxzuOuCZkB7KUSQs8xFKDu5+bvXoMWRCDCAYGgJ+4XdDpBv2qHaxoGiF0KgVMK6VS/IEmEcOBFyFWInbPfI8z50PHa4HbUz8P7YC8AIXqHbj8UY6GIcFgvsEbQDSOH+7FDLNYih8E4dx+IJGUgL/b3qlBY3ysi4sI9ECuBs7NTrLdh5Pfdd6ixpMskbVthre8X7oDedaYJx1UIfJ/A2Lf24WChiBbBcYkXhKKoI3iPqf79OB8RhB7Y6WEDFVHrvigTKqLbgaCpEo1KkqA0UAqpDLbpwDp0qtE6GRrNJEn7xlNirMFVHtN2eAsSxeJg8b3n2Q8WBE+fPh0g6fF4/I4DXxwbjEYjjo+PGY/HAzHv3/ybf8Pz58+5vLwEYDab8fOf/5y7u7sBgn/58uUgydNac3t7y+PHj3HOcXx8zLNnz3j79u0gbzw6OuKnP/0p/+E//Aem0ymfffYZd3d3vH37dpiRxTl7XCRj/kBZluFDEmJAGXa73YAAPH78mLZtGY/HPHz4kNlsRlVVgzVw0zQcHh7y8OFDTk5OkFKy3W5ZLpdDURFjidM05fHjxzx48GDYr/Pz88FzIQYiHRwckOf54MoYUYI4Bon72XXd8DxRmvjy5Us+++wzfvSjHw3OkFprFosFdV1ze3s7cBrgXRlg3KId8w8t2h8iBr5fUHwfB2H/5x+LUAhgNztqpVA6GHa0XcVqt2JTlzw4Oefu+i1Xb5/i6x3CesbjKZ//5OeMF1O8MdRNTbnb0XQdbWfDBe0sB4eL4K7ZtCHf3XtwHevtimW1ZlNu8dbQ+hahEvLJiMPTI3xnqK5XMCmQWpDpHCsdje9ASjpjkV7w+tVrlss11lgUArOrcdQ0bUs2KqhMi7UmQNlScLvaMB0vGKci3Dxai7DEuyrgA4FQay6/u6VZ3zIaFTx6dMZCBfXFuupQZYrSfbOiNdrnCNORpSmjRJIgyZOUfFSwXm9CZ9WsOCgSdkmBVzlSeKzd4FTKT37xmNu3l8yOH3B4/pCT83MOFof4Fra1wVu4u7vGdW3Qgbcd3hl0qnE2RYqUrMhRoxEScLsGa8HQcfzoEcv1inXV4OWWo6MzTs8+pzh5zHhxzGhUBCManSBFiNbFdHRti7MdtbFBz961CNeRYJDWkGqJ0oK0LZFVia4rTNMOrqIfY6u2K4TQMSOHAR0YOsp+ztz/SPQraD/5ZpD79Xnesi8sghdAWHAjuTBK82LAUYDx75HNvlfGuRDfHY5Lzyzom0TXL9zByE70aLVADIXL8Cth39y795r7MUavUoqP9/04Q4p+/BAkh+FPPyYxvaSyn5dHRYVKgtJBS02iU06OzvjR5x1tHQjGF1evSZIRWgdkRfYmP3VdUrcNvXYLcIh+pCJUCkJhurL/OMKKH0EXpOx/LxRSjn402I8gkkRTpDlJmgV+U9thrCUrcsaTKW3TUpUBXRYStJLhM4yjBgeYYCGeaE2S5ozGY87OH3B2dsrhYhEKKGPYbXdcXV7x4OGD7z3PfrAgOD4+Hsh1u91u6BCLohgIcXVdD4vvbDbj+PiY+XzOo0eP+Gf/7J9xc3MzjBPW6/UQ9LPdbjk5ORng9jRNOT09Zb1e8x//43/kyZMn74QOnZ2dcXd3N2QBvHnzZui45/M5dV1zcXExeBwIIYYuviiKd5wE9080gOVyOXTZUYEQLZXLsuTP//zPefbs2YCM1HU9jAFOTk54+vQpVVUNBcf19TXPnj0bbJvn8zneew4PD4dRxOvXr4fMgsijiOgCMHgb1HU9OBquVisuLy8pimLgS0TfB601n3zyCQ8fPqSua66vrwe/iDhC2N/2O/0fgvO/Dy14/2ff97sfm0/Q7baIVOOLDO87unJDs1qRWMerZ1+TtreMuxpEwuTkhLPHn3N4eoaxNa1OmI0nVHWN3e5wPmZs5GEB3Wxo2g6BRHmPb0pMa6mbCteVtJsWqQpmh484evQ5Nsm4vbtlvbxBJZoOQTIqyCdjkjTDWk9RpFx+94bbmyUeQZJmmM6wuVuSpglZkYebsg/6bu88jQnpJdu6YzSCNFEkSSguAgQajFKatqbICtJiTLW6Y7Vao5Ukf3CCa1vWux2IjDSVjCcp0+kISw5rzzgdkWgbcmgFpCLhcDxnlBc8vVlS1oLWduhcItMU4SeIVmI6z5/8D/+cL37y85DW2dSYzmClJp2n3K2vEFVNW5YkHnKdYGUwrVE6p5hIVss7dusd6BR0hjCeclsjXMJifsjs9IzDh58yP3tMPluQZqNgS+wcriyxNihLsIZcSgrVUbcliVSosqXe7NhuVixXN1xcXrFcrik3d1S7FXVV0jZNGO04z//t//p/+jgnspeBVKhjeoYPa4yzeMw7UHXYBEqowIVQSYDwB2Sgh5f7xdnjh8V/WK19b90dyAB4H8yjbJx3+3vFktL3HiW+h8MhkOOapu3bYYbXii8Tf18ioPfXVz3ZUfRKARUXfnkPjcdRQf82B4VEeH+KqLZI+kImEiqFjOTIYA0sleL09Jwsy/HO8+3Tr0kyPZjQBaJ2RaoSklzjZRh9xLulFIo0zRFKU5X54MLr+2wSIcP7ieNKKcRgoITwKOHIlO6lnAFR0FIhPJimY9XeYZ0jOp2rPKUYBaWcaVvOT045Oz5mPpsxnUyCtD/P8ECaZT0fxA0F3MF0xuHBnO++++57T7MfLAgePXo0QNVxgW2aZvAgiNwCgPF4zPHxMUdHRxhjBu28Uoqrq6sBLTg5OeHt27ccHBxwfHxMkiRDnPDbt2+H7ITf/va3NE0TKiMhuLq6Goh7zjkePnzIbrfj+voaCEFMUYEQuQNCCOq6HtIIhRCMRiOaphlGCZGDEGf8USngvadtW7Is49mzZ+8EGR0fHw8GTcvlEuccbdsyn8+HccPV1RX/+l//a/7pP/2nnJycDN4FAHkebupPnz5luVwO9tDxT0Q14kilrmvu7u64vb1ls9lQ1zXT6ZSrq6vhMxiNRsznc9q2ZTKZ0LYtFxcXpGk6vKdoirTvMvkh2+Pv6/rDhfyHTYY+NDb4WFvrWzLv2NzecX35ipurN2w2W1bLCuk958eHCC9YHB1z9OgRs9MDUI48GZEmCY212NYhE0cuEzSOpqqoyx0O0buMKbwUpFnB0YPHdMpye/kS6pJidsTx2RmLo0OEEpTLNbnSdDYw7etuy3ZThkAl6zC+I1GK8WRKU7e0TU1rOupqx0G+QGcpWiuscX1wWbjJeAF1a3A+JBpqrVAyKCfwIQina1vkeIRPMqwMZNt2W/H1Ny+ZZhpsg8Ojxzlkc4TLSbRhPE7B1WjrMMbR1BW3uy3ltmGz3rGrdnRColJBmiim0zHH52ccnT5kdvaE6XzKdJLR7DbUzZKmKhHZAZaETIY5fSYEqYCkn81OxhO2fod1lsnBlK5paK2nxdBstrTtltMvP+Pg4afks0PSfEKqE1IMqlkjmhZhOtq6our94jebLettybru2O0a2m1FvVlS71a09Zqu2dC1NaZXStCHw+AJUc3z+Uc7j48WD0GmfRcfbvDSB7hdyB6qFyCERUmPUh5nDcZYms70rHzfL1oOYy1t2yMe/Rgh3gNiEQCiJ1yK4JI48CwCnyWaAUXOwn0tcc8pkP0g3/cohY8cBXk/yhTIviCIJA4CBO/u+Qn3pEbRs/X7UKOBtHhfvDjX8w0iIQ+AHoXoSXVCCrSWjGRGlh7jvWQ+n4XxmrdUdcV2u2W72fbN7Ianz55SNXUvvxQ44YODoOmDluL9Lrqh+uj0qCBJkK4vmIxBOkuKZ14oDJ5icYxKMqRW6DQhy0akWUqaaIp8wtMXL7hbrUFAnmeMDxf8b/7yn3B8eIhWCtV/NojwGlKJ/t4eSKIRdBmNEg7m4+89z36wIIjd6Hg8HhbZqAaIC9FsNiPLMmaz2ZDsN5vN2O121HXNq1ev6LpueI6Liwucc4N/QUQIrq6u+PWvfw2EyjGSCIHBoyBWoZvNhhcvXnB8fMyDBw8GF8N4Mkb/g7ioRrJiVBrEBTDKCyFAKvvSviiPjIWE957lcomUkocPHw4/894PcdBZlg2OjBEF+fWvf83x8TGnp6ccHByQZRl5nnN8fDwQC1+/fj3wKqL5UNz39Xo9ODXG0UckYcaiJh4jCIVZVD5E1cZqtRosomPkclVVgxHSvpHRh7r/99UEP2RNvG9EtP9c+14Ff8zNyY7bm0uuXn7H7e11cMf0isw6FgdjUp0yWxxx9vCc+eFB6MAEyCxFJZqm7FCJRJkQaeo87Ko1oyKn6ww4T9ta2sZQC5iOClQ2IS3GYDTTxSF5UYDpsJ0JZCc0XWexTYsRDqEVoENzZw2dtUiVojPIxjlZlmLaBoRgWozZrbe0bRNeH4HSGu8FbWtx1qGyhERplDK9VXLPQu46LBI5miOKGW1Zs9tUtNUGO0kYKUGWaYzv2PSyxekiRTpBWVdsqg3r7YZ1WVI1DbaFy1UFXjKeHXB4dMDB0SEHi0OmBweMixHF9ACvPK5ucWUNTYv2Hp0Iyqaicw2FzED2UjEl0VIgtEZbRyYk1iTYztNs1+yahvHhgkcP/4TpYk6SZijAVRuasuVmvWa5XrPdlNRNw67rqI2jtTZ4UHQNzW5Ht13T7TZ0zQbTlYEwaFoiVB1qAEUxmbM4OeXB409YHB9/lHMYoGq24HXgEQgXJ+Th2lIeJYPZT6KDvFMqAcIifFh8QlEYuvlUJAHZHlyOxXvFQPhu7K4hMP/jYtqXBIGwiBgY+MMkYI9jBP29yTsQwR00EOoC6iCRwWzI234EEN/x/UhE9uMQ+qLDxz0YkAHu99H74PET+RQusPGH8CbR2zIPlQcDbyJNwljJI1EjQZ5mHMxmdMZSVw1VVXFze0tnLUolTMYTHj9+hHcO0wYL+V25o+vJ40mSMJvO0EmKSjXb9YYXL55xcfEGuo5MSJ48eczdbsfPfvELZovDwCXQEqVStJIoAVpnnJ0dU/Yk/CTRjEdjTo8PSZOUSOT0LoSoOTzOuOBkCQOB+R3y5/dsP1gQxEUkktqiD0EkCEbv/+hUOJlM2O12AyzufUgOnEwmHB4estlsuLy8HKR18e8RZn/9+vWQsBh9AuJJuR+37JwbcgViSuJutxv2ERjIhvsJhpFTECV+sSiIM/aYdwAM3IZIhIwR0NZaxuPxYFAUjZYGA5xeAjmdThmPx7x584btdkvTNFxdXaG1Hrr00WjEyckJWusBAYhWyE3TcHt7y8XFxTAeSNOUPM8HK+VIHowSxFjsaK2H4ix+FvuukfHYxvn+/vggXtD72x9SEez//fuIhR9LZXB585brb7+hvg4XslcpWiecHcxYLMZ0csTZg0csThakmQ5NhJQIDU1X0nUlXVvStDXehosTrcjHOXazw7ZdoBUpBQQHsiTJKYoJicjJRmOQGtMGfbNSwaynaRu6usJrUCJBJYpiNEamI5q6o26DQmA0nXByfkJr2gC/Nh3dyvRomUMIFTwCkNRK0bQd41EW5olaUbddgC9VkDkZ60iKGdn0mHrXUG5usXWFUx2lTvBah5u2rajcHXXnWG7WbJsSZVvKqqG1Jsirggszk8MTTk8fcnR6xOHBnOlkTJJoBJ6mK1FW0JgW09bhJtdD+k1rEFhU6hH9fBohcZ2hc5aqrbF1w67c0nUNSZ5zNF8wXhySpCPubrdUu2uasqWuWqpdzbas2VQVZdfRGYPpY2udNfiuxXcVtlrhqhW2LQMS4A3CuZAJKCW6yJgdHHB0esrByQmL4zNOzh4M47yPsTnbIkXocoWM16rqkwTtYB0MDus83gS3QecEQmpkz2KTUofZuNhLM8T/3vXp+xFD+Af96MnfA+Y+FCb7IthhnRFB/hpHBUCPQATEwLke+Hf3skcfUpoGBUIg2MV8vv1CJUoQ79GA+MK258zsvez98XNuMDEaCp9hf4fW+p2xixCQ6BStIE0yfvTlj1gsbrm7W+GA05NTvvziM5y3Qa3TO89GVDvRCaPRuDfDE7x69YqLN696JLijFpKbzYZt0yCUYjqbU+RZr6oIY4VQKEkW8ynz6XgompIkDZLkvkgIxzM2bf29dzhu98iOGIiQH97+YPwxMMDpsepLkmSY7Uf+QGS5R75AJMJF45yiKLi5uaFpmmGB9N4PBMXNZjMkAN7d3b3jNBgh72jxm2UZ3ntubm64u7sbooQjoeX6+vodCD4+R1RJ7C+E+/P7OBKJi2qURU6nU6y1lGU5dOZd13F4eDh4CcTjEmH+7XbLbDYbRidxMY7vo23bd0YJEXV5fxGPvgKTyeSdIkwpxe3t7VDoRKJknodZVrQ9FiLYTEfPhDiOiGOK96OJ98mD748HPjQGeB8JeP9x/618g3+o7dlXX3H3zdfMtGY0P4AsxRnLYjKiyDRSj5gt5iR5FmBIHxLJhO+4W14Fb4fdjqoOPIORSklHGXVXU9U7yl1DlhXkRYEQYOsO0xgEijTXCKUwxuK8Ie2Lwc42dK7Dehus3I0kSTzHZ0ckk5TduuTizTVt16K0Ih8XJLJAVjX1akU+HbHZ7bDG4m38HCFTOVXTYl3oItIkQYom6J1leG9d25GlOcV0QbndUnUVfiuxxrMT0BiBxWFcQ9p5tnXDy++eYazlcDYhz8fkWY7xnlYaRlJwcHbK0aMnTGdTsizc/KQ3GO8p65o8TcG2KO1JshxUQlc5EusDOxrwQmC8oDOWpgrxs7e3d+xWO3ZVSZpp5gcLiukxXSO4ePWc2/Wa7balqlqaLrweSiMTjUhDgqFyHmEbfOewwmK6BqcNbuxxeUiwxAeIWScJ2WjE9HDB2fkZj548Yb44IkkLhNR0e3Hrf+wt1QqtEpTsb/iEBE+kxHvThxQJnHXBW8LbHqIXINSwODjhCSl9YfOevnu+75ghRhqLgdkfEw7vF5N+kYmMRB/Z9f2i7kOBfL8I3/9qKFxEX5T43nfvfhsUBf0O+n6xgw83ILGo+T3ychwzEAuc+8bk99xV+9HCO/cpEfcx7O7jx4+Zzw94qV9RNy3z2QytFcY48tGov97EsN8RTQmbJdXBRREpsBIqPL95/QqU4tHFJaPpAdgJ0jtA4YQLfwhjknj4PWBaw75kdtjl+H56wqgU9548Ydy0R+r8wPYHOQR1XQ+dNvAOq73ruoGNH2V9MbNAKcXFxcVgsLPb7Qa4fLPZDBbFq9Vq0M5Hid9utxuY93FBj+TAuCBHo59YeMSiYX9fowHRvllRDDGSMiTSrddr8jynKIphXLD/XLvdbjAmiqZFSqmBVzEej4cuPRoDxT9d1w3kxKOjI5IkoaoqXr9+zbfffss333wzcASiOVLs9F++fMloNOJnP/vZEGwUzYxivkPTNPz2t7/l7OxsKGim0+kw/oiLe0R2FosFbdsOCEyUN8Zi6kNd/D5q8D4Bcb9g+xD5cP/vHwshuHr6gqw26GlQx5BkbLZ3bJYtavQAkWkstv88QbgAtVkrubp6jfMC6wyNqcGr3uzD8fzlc0xlSHWOQyCT0JFvNivevnpGomEynoCsw6xWpyQHi0Ao8pBkGShJ07Z0nSHNHWcPT+iUp8gL7i5vsFJim4bN8o7DB2fsqh3jwznH52eoPOfiu4tAtpOKtq2pTMq2NbQuWCRnWYqWktZa8A7lPF3bkE3HyEwhE0mSZnRJwbJp8KmmM471Zo3xjuOTUzKpMW3HeDRhMjvk7MEndF5xcXsXTJ62K7q2pGtqTCXpfEA7bC+nxHhKUzGZjRhlCXhBUxuMDalzWV/4V23LpulY1y1N2aJNx+2y5uWbNePRiJHUbN9u2PzumqY1JNqjM0WaKqbjcF9IsoIkLdBpgtQeaRWi65C2QrgKZztMW+Nsh/VdWGS8x2NBQz6bcvboMadHp9B3lJHbYV1Llv83ZcH9g2xSOlIddP/W9vcnA8aDs7Z3FARPLAY6fGS2ez+QCgeuQD8SwPug/d9zNFRS9eu1RCvdL6wqfJ5if34f9qvH4YdFvO/dQ8cqQsHhvB0cEK0N1MS40kbJIkSXPgY54v12fx95//4Su+CIdLwrt5b9Ih0+64BC3fMdwuMIcsx3epb7EYrtCyLXr0Oyf87tdsvf/t0lZbXj4dkDHj18SN5z12IrL6KREJaj0yMOFnOSVwlt1+GEpnQGKTTbOuRpGOdDOicOFHgVFnVF8BeRUekhouojKKiG0sfvDwQ8PWWTKO38oXEB/IGCIGrjY6cfZ+Mxuvjm5mYw09lsNux2O87OzoZwn2g+FK2HX7x4McT9Pn78eAgv2mw2WGv5/PPP6bqOi4sLrq6uhsIixia3bcvnn38OwM3NDVdXV4NDX1xQo70wMHj/R7XCbrdDKTUEBEXCnfeeL7744h3r4YgufPPNN3z11VdD2NCzZ8+IioGiKIYxhDGGk5OTgVB4d3fH1dXV0JnHMcF4POazzz5jsVhgjOH29pa3b9/Stu0gZYzx0p9//jmPHj0aIpBXqxWvXr1it9sNHIGLi4vB1TF+RvuSwn0nSCEETdOQZdk7Fsv7BMP9bX+MEP/9oe2HHvPOHPEjbLPRjGJ+hBSO9aalbbfUTYmROWOVMS2CZ4UHslTjrKHcbRDC0WyXtBiqpqIydSDoXb9mdbehLGuUKDg/fshYKbwJAVNXVxd0jSVPE5yp6XYeJxNUAVIJRpMxm12JNxYpNEiLdZa2JzKl45TL16/wbUcuFdI4mrsddbYJSgYFxluefPE5s/kht5c3mKpls1xTbtbcXS95sFigxwl5CmkiaawlUhDbtkGIKSoJo4Fqe0tma3yegtNoNLPRlNFkxJPPPgkWwE3DycMnHC4WbHclm/UaUs1sdkDZLLHVis31azJxhmaM7XqSk+tIszHSJ1QdIXLYeVxnMKbFC8+mtlwv19TOoRNNrhKyBJ69veWuqnh4ekBRpGTjETorEDIlFTleJ1jvaJHYXonlfTCYqmpLV66pd3eU2yVtvcW2DaLpoC+EW2uQqWe6KDg+PuDh0SHnJ8cUMmEkBMl0jHWerrN0rUF0BlPvPso5DNDZJd7XYSZuLcYES9sQbCSx/l14GGL33ksGue+MpQh8g35C03fL8XcFCNebBQVkC6+CoiFKAX0oOug9DoI5Thi7DvHLfv9eECHs2OX3kDzBancwPoqciL5QuVcPREOloKaSQr7zXgdfhkgkJCKdPZmOQFKM33+Hp/DO5EAMiENESIChqOm6jtevX3F5eY2xYfzx4rsXrNZLZuMJ//yf/TOePHnCKM+D9DA8aVAmKI+SiiIrGKUF1bYOYzKhUTrl5OiEk5NTFotZ+FxQfUHUjw/48LjWex/uS7yLyvq+MBhkon2+hPNxVPPh7Q9aFx8eHg4z9pg2+M033+C9Z7PZ8Gd/9md88sknQ2DRzc0NaZqyXC7ZbDZE0mBk7EeL4d/97ndDdG+MT765ueHrr7/m7u6O09PTgbAYRxNN0/B3f/d3gylRJDUaY4ZuNy5+0XEwyzLatuX29pYsy1gsFsNIIc7k27bl6dOnJEnCv/gX/2IYCRRFwbfffvtOxz+bzRBCcHt7y/Pnzzk9PaUsy+E9LBaLAQlZLpeDWdFuF24mUZ1xfn7O9fX1oM4YjUZordlutzx//pwHDx5gjOHZs2es12s2mw1lWeKcG8YCi8ViIB2maTrIRKuqYjabDQt+HNEIEQyUhBCDJHK9XrPdbt8xXdofF3worfD9UcCHSIX7j/2YWzIuEEKQJQrTGSYHU84e/ynTo1OMUxweHaBVgtYSaw11taNqdixXl1zfvqJsdiiRYkVC46BrS0zXoCWkUiKcoa02VKuO9d0K2znG44JEgbAhHMh58F1LVVewUWx3JaAx3jE+mPP49JDD4zlpkSCB6WzCbrlDmBDItFtv2K63IYVQCSbzGeWuRAtNKhKM9xTjgnqzYrtcU1UNQkkynZKmGtm0fa/g6TqDNZ5MF+T5mLelA6NQGIyu2G53SCFxOC6+e8F4fsCPfvHPaW3N3z37ezrbUBRTJuMDutpgncV0Nbar8LZFyV4WZRqyIoxhEjxd0wTOnvO0bceuarnetKjZEeMv/oTH8wljCbKqqLdbTn/yJSKfkmcaLX1wzPOEFDof+Ae7xvL01Zrr9Yp1s6NrK9rNluXylvLuDlNuEW0FXY3vWrAGKToWizmzxQQ9shw/PmU2WbDbdfz626ccTFNOVtccHh2FHAylkc6HOFr//aEw/9Cb1gdIkeOFRUiH0oI8jxyCCOfv2RXT2xDTfz+SyVxEECxCuJ6sFwqpsAbud9jh91z/vgWD237YKRGtnPvunF42p+7RhvA8Ym+/wuOVUn2nHdUNsakOfxF7cP++pHEoLPz+zzzOdYE74fu5u4sPiciF7ZGK/jEu3rd64p0XQ+RzHyI57E8g7YbXOzs7oTMdy/UGnaT87E//lJvbG169fMm3L15wdHw8rBGqH6cIGSSQSio+ffwpV9e33K7WeBxKaQ4mE85OjkmSgPQFk2PVRzD7YaAyICn+/pjERss6O8gMe5NknHVBbrt3r7Y28B2+9zz73p/AsNjFOXbTNLx9+5ajoyPm8/mQVxAJgs45zs7OAhvz5gYp5WD+o5Ti+vp64BBEI6B9iWDslJVSg8Kg67ph0RqPxzx58gTnHG/evKFt22H2nuf5sGjHGX08Ia21/PSnPx2+H5MSo8QvFgU//vGPh0Xy8vKS58+fMx6P+elPfzooGaJzYlEUPHz4kNFoxG634+XLlyilmM/naK0HwmGSJNze3g78gmhAFDv3PM9ZrVbvaEPjft3e3g4EQynlEAl9d3fH9fU1v/3tbwcFx0Bk6V/n9vZ2GLXEYu7o6IjHjx9zcnIySDZfvXrF9fV1kNj0n0ucOe2PbOL2oWLgQ4TD7xsn/LG3yrWgNNW6Yj4/4PTxA84eneFkhvWCfJQEbb0LAULRka2sK8qm4u7qBixkxRRdTFGil+K3hsU8Y/n6FTcu6P195xnlY0Q6ZldWKAF5rpE6QagEY0PXkaQJrQEhFfPjBQenh3SuxZRbMp2yODzm9nLF+m5D07Q4H7IvlJc0bcfli7dUdY2WilQHljEKHv7oEzbLO3beYLVkWuSMtgXrXYW1PdPbO5quIU80WZrTliE2OPGeg+kJM6DaVTS7jnKW8OM//QucGKHrOw4PFwihyNIxSiSU5YZJPsEKwXg+JxuPSUdFIBTW4aYmjKfIYLVrudvV7FqLSzLGR+c8+ekjDo5PyfMs8A5EKFqO8CHKOZXkSpL2sKfF03rw1mKM5eL1W3737A3bmytu767ZrJe02xW2XmOrBtFZhLCkiWByNOPw5IhPH59zPp/z6vUbVAGPf/IF09kZN9+t+Pu//iuKkURm4x6pWzMajUh0jugs+gcsX/+ht+W6RIouLG8iLvP9sJ171UGEw70YlrS+uI8wfRwBRhC+75L3ruNoEQz3C25ITuyRTpn0Frrh9WU/y45zhMBFjDwB3pvl++H1ERKP6L3796IE+/25n8OH9xhHwSFrIS7qQePvfdt/7aOHrcXZXt/vLNbexzh77rvkgVegFIlOw/EioLmqT8YNcu0EjyPPAon+6vqWv//Nb0nznMPjY3SaUZYVd6t1CMmKn48M+0vP1dp1DePFjNliTl2WpDrl8eOHVE3F1dVFIN32FpRCiL6oUwN/QAz3UoHztieOhiJoGMvYQJx3PkhMA/rrg+ti22H/ewuCy8tL5vP5EO8bXfQi7H15eYn3fnDGm8/nw4w/LrLRyW+1WvH27dtBBhdli/sEtvV6jTGG8/PzAeKOcr5oa6y1fscIKJIMb25uBr//SAyMC6vo5z3RSCnLMoqi7xyzjCdPngDBbfDi4mKQ/EVTppubm2Fhrut6kAvG8UfkM0TOQZzZz+dz8jwfrJSjTbNSaiBD7na7d5IKjTFD4RDHJHmeD6ZMRVGQ5zlVVQ3JjvH1Ivkx8guis+R+YbRPSlwul2RZNhRkERWI/I4PORy+jxrs//kQOfF90uIfe7NViS+mHJ8/5PhowfxgggKKVOKlZH1zC1aA8wG6tzXr3Q2b9RLZKUZ6itUdAo+tdzTOIJ3GtQ2Xb19Rly15lqFlgreerqqosBjpKEZTaidJhWaUF4xGE4QII6TWGY7PTjhYzEkT3TsUKkb5mNurG8pdGWatUiC8pDNmmO2miQaf9XCgoOks49GYk/NzvHNUdUXXGbJFymQ6QV7fhmEzYTbZmoY0Ueh0BCQYD0We0TpLAiA82aRgdv4AVYyRbYcxLaNihhM6ZM47RyJBK01tWowQOBFIbV6E+XtbWqpdxRUOlxQk8zNO5oeMDw4ZHRyQj8akOrjQSwRKCVIlKDQUw40x1DHOBhtZ7xxN63jz7CX/3//PX/G7b16wXF1Tlxu6psLaBiVaJnnK/HTB4mjOweGc2XTGOB+xmIwZJyHf4eXlW65eX9NsDO02HOvVzYY3ncSbjlEOs2nFqBhjnaT5iE6F3oKTflg8RU/yc3tM/RCjy8Bji1yB4Oo3tMz9FjgHYt+xsGfzyX4Rj4t6uP5D2I/t7XyF7Xq43feufe/6AjjrhmLintTu4//6xV4Ozx9n/OFeErr2iFrQjyICSZKwCPYFQ+AlODymrz3k3vOIe2Sktzn2Q0Ek778vAOMG0qiL3sL9+3e9hUFEVsqqpCx3dF1LZzukltR1S5W0PH/5HTfX1/Q7+g5vwlpLa1rKukJrSVHkaJVwd3vLr/7mVyQ6BGxFOWUsqO75HXG00n/ivVOkcw5H/9Xb++PSH69genR/YtwXWb+//WBBsM9Ejxr2SD67vr5+x1Y3Su4uLy8HeWEkJAKUZTkUDhHaj6z/SM6LMHvXhYz2Se++pJQabITjLD3OmuI+np2dDS6GkX8QO926rinLEqUUDx8+HDIOsiwb4PrpdMqvf/1rXrx4MSyy4/F4cGiMJL2mabi8vBzMlcbj8SBRjAVCOKnu0wvjOCB6HgDDz+JIZLFYAAxFxW63G0iUcXQTL/Lb21uAweExwvwxdbLuPeHj68aRynq9HsYoUZrYNM3wO5G4GD/z6Ja4b9n6oc5/f/GPW/z3+wXFH3sr0oSjk2AMNJ8VjIsssP19YN27uqVtDFHJ5LBgBcIJClFAApWvgF7b27QgLQ7DrmpIVYoxLUJCluUoJF3b4LIEp0LSn87HFKMJSZYFTg0wGhfMZpOQYEhQBSRCUW52LG+WeNtHz6qo0+47NxuUBYkMC2ivBmO72vLm2Xds12sS4SjLGuNcsP3VGtH2dqteYtsOV4DMC/RoQbXc0lqHb5qQjGhDYWAc3N2tOSxShHWkOicIBcOi4BxIZ3snvw7TWaqypvJgrME6hRrNKWYHjA4WFLMD8vGUNCt673WBEpBoyKQY/qi+wzQuzEe73hyq2dWs7+54+eIVv/mbX/Hbb37LervF2QYtDGkBiR4xnRxxdnzO4dGM46MDsnwEKITzjLIULTpOzo5Zb0vuru+4vboDoXFaULeedWtRSGxr8duaprF01tP4j1fYOheCdsM66Hpi3163SFzD71nk90ThYJ418M4Ii8I96U72z3W/qIvfKwjokYk92mC8B3hDwNnvr/UoiY7BRQMIPyy098WIj0qFWMhAzwnoCwaCBz8ED4Hfv59EiV34uXhv4bs/SrEY8AP8HjkFgYzp3rm3ud5IyFgb/piQXhpdeeezKXfrFeVuBwiSRFFXO7q6DIiA75VuzoV0w0iudA4tBCpJkFLRtS2bbtWvV5ETcS+N9NxHWg9jlPBG7j0GiPyNe96G6z+rvnYiFhgx8+FD2x+UHcbON87sY5LgcrkcuvGoPIgEOynlEFMcyWv7YUMxDyF6EOzPruOYYR8RAAZFwNu3b6mqatAER0j7yZMnvHnzhtFoNIT/xO59f0H85JNPhgslLtJlWQ6kxMiBWCwWQ5ETT5I8z2mahru7O16/fs3x8fFgR3x4eEhZlsHHu19MhRCDIiOaBMX3GrkFx8fHwzEYqsg+rAkC5wAYVATL5XLgZ8D9xRslm1FRERMggWF/YiKj98FMaT6fDwWVEGKQL26324Hz8CGuwIf+/qGxwod+9sfezs4ecnL+AKVBy0BiUioJvbKDRGlausEsxXqLVimpzmi7FuFFiDWN/3mHcQ7rTZgBJhpvHSIRFLMCbxzltkWoDCcUQmfoNENqjXMeY0IRPZtOSbQO8zzpUUrQtQ13l7fU2wqJREs/xK/G/cN7nOnQUpEIiVAK0pSLmxuq1RZvOlIlubtecrSYkxUJqU6oRYXtOzlngoGR0CnJeMbyOiV3DYnzOAFSa3RPTK3LGq8DqayPsMN5F7qjpsXbvoPvLE1j8N4hVIJMc7LxjOnJA+ZHp4wmE3R/PeFB+xDBnCaCNBFkSpB4UN5jHdTO03SWpgnFfLnasbq+4fXLl3z9u294+fwZjduRKE+SS7J8wmhSMJ9Mmc/mPDx/yMFszGw6xnSOsmyw3qETxShPUd5zfnrGdleyXN30xDFQCErrSVSCFxLpBG1lqLY7Kvkx+TD9RFwEMBpcT8aLhMC4mMthbY0dZbz+IgJ47+oXu9BwXjMsIPuPp//5/dw+/sw503f7IctgKFY8w3OF5aynDPYLXZTDiT5jQMQ/8ecyxm1H46N75GHwNwg7sdcvy72aQgzHK+63C/8gEh9djzYNpOc+qjw0MvbeTthZpBFgAG9xSLxXjIo83D+tASFJtGaUJyEsa69gU70Nt1CSd1fzviiToSBTUr2TuulFLH7Ce7hHcO6PZURPhs9qmPOEl3D3p03Pk+gLoB84y36wIIjQeGTqR2ndvu//zc3NkBUQLX0vLi746quvhrHA1dXVO0S+GJgUcweiT78xhizLaJpmQAGiA2F8TFQkJEkydKT7ccYRtYiLZOQkxMLg6uqKg4ODgUcQ7X0jNL/PE4hbLGYiWqGUYrfb8erVqyGiOZoVSSmpqmB7GVGMGHoU9y3uz2az4fHjx4zHY96+fctmsxnQguVyyatXr8jznMPDQ9brNd999x3Pnz8fjmeUegLDMYtJjlG+uQ/bR4OnKA0FhjTL6LK4XC65uLgY4qWHk22opt89nYabzN7IIX5/X7L4sbZPPvsJUgvqco3twKZFyC5PEpwxJHlO4gVtVVP3fgMqUyipqe2apqvwwuKkxQiD1wECbI0h1Ql4j8412SRHT1PaqsW2IJXEEObenTHUVd0Xy5BqzWQ0AmOpt2VIrJOe9e0SU7bBN8D3EbQ9fOsFOOPA0N9QPYlSZHlOkkuuVreILhTAd1dLXudj5rMZD54ck+UpcleCN0ifYGwI9fEWhAYnNUkq0UKgs4QsVUwOZixmM6azKaat8NbQ1E3vqujpmpZd3eDliNGkIEkneFkgRwvGhyfMT044OD4kzUfhZuYJynlv0EpQpJpRoshi4+oD6lB2jra1VE1HWe5Y391ye3HJ9cUFb16/4tWb79hsN6ixZJHPyLUmTzWT6ZjF0YKjxYJEa6bFhFFekMoET0uaKFSWIQQsDqa0ZcnZw1N2dYNFsN6tWa+XKC0Rnaa1DpGPSNMM1xg29YaN+3g+BLGJCYukx/uIBNz3wXGMH7vf/vINsrthke5he++HsYAgFgRh27+Ow2+9S+SLi1AcQ4T9iKMHNSzqA8wtNUKG8CGlQ2iQ7gOJAjchKB/unQf7eObY3e4hF/tNhhi4D3FcErt7148V+q/OYV2A/I3pBo5BmK/Hx5geAQiqH9fLj13f4Uflmeubtq7rmI3HjHr/ESkDofL+ffTvISLoMkg7EffoR1gLQkEQx3+xIID7r4Hc+K5kNPAL3rvX9smTDPfd+4IoFnrO3as9PrT9YEFQliVXV1ecnp6y3W6HMJ/j42P+6q/+ajDLMcYwnU4Hj4G/+7u/G6DpSFaLUcZxnr3dbrm+vv49K+GoOtg3DoqSwf1uNna6EWL/1a9+NUDh0dgn8gyiJXGE7w8PD4duP4YUHR4eYq0dCiCt9WAs9Ktf/Wog80UIP5It3759y9XVFb/85S/58Y9/zD/6R/9oiD2OBkvOOQ4ODgZo3ntPXdc8f/6crut4/PjxcOLEn0US5Gq1oigK3r59y9OnT3nz5s3gSxDn/BBGDXGkE2WFVVWFD7mXPXrvB0VEWZbvjDf2RwfT6RTvPev1esh12N+/4UbR/33f5GN/lBAv4P3C5Y+9KZVgzBbvPFplpOkIlaRYQu+CDBdu1xk26y3bzQada4wtabuasqqRqUKNFEhD3dWITtJZw3g0g7KDuqXxPoQTTUak85y2c7Smg6YBITEukLq0VpydnoCzdG3D7e2K1XqNFY50knJ0uMCsgnGUQKKEAhlmslIlVG1N1xhkKrHO0lmDE4L5uGB3u6W8LSnXDW9eXTGbj5iNNcUoR64ltBaPRSDZbbfsNiXb3QYvIZEpdbVjrBNM0yGM5Wg+4+DogLvXK3blmto4cj1FeI3DIkcLinnO4vSEw4dPODp7wOzwiLwYBUtaY7BVh1SCcZGQZ3pAA1ICgdB0YKygs47adOyait1mw+ryiquXL7m7u2a9XrLerCmbkiyHLJ1gnGU0GXF4eMDRdMpiOmVcFFgRirD5wQTXdDRliQOSLEHlCTiHNR1KeFIlOZhPuVstuby5wBhD3bQ8eHRGtSkZq4wsHdMKizoWTNrmo53Hbdsgpeuvpb4PFr4vEICo+6efIcE9Sgp7C/dep9h/w/r9OPP763a4roXAE+WBfTBSD2+rvruXUg0Lm1KqLz186OhFjB/u91wEO2TbM/udtwMHwXl3D/vvNR/hnrKvfOo5Ct73C3coosNibrDG4HC9TNPgncU6s/eYvlGyPTqNGLgDbrjHCYRngPdJ0+G9DyqK6KyIGySREA2Awlf65xiOcX9MBxRH3jdPcXEPKZT996RkgAr3CoR3GjThQcR02yhTJCZbE+kc3H/54PaDBYFSihcvXvDtt99yeHjIYrHg1atX/Of//J85OTnh6OiIg4ODwXDnd7/7HY8fP+aTTz5hu93yJ3/yJ3z33Xe8fft2UABEXf5msxm68thVr9frgaEfpYPRZyAaCsVCIJoORQ+Ew8ND3rx5w5s3b4aFMH5wTdMMBkJSSiaTyfD+/uIv/oJf/epXnJ6eDrJIYFBAbLfb4b2/fPlygOTjqOHy8pLZbIaUkru7O7766qshPTGqIQ4PD0nTlHkfjhJdDOu65uXLl1xeXg78h6IoBvvL+O/tdsvV1RVlWQa2eU+0jCTPyJOIcsPIddh3mIzHIkotI+EwEiuPjo44OjrC+5DZEMczV1dXbDaboTCIxzUu8u93//t8gveJhh9j67qGsqwosoQsz1CJxNiO1lmaukIBq9WSsqvR85SD6RHlZkO5q9iua2wbbrSdNRhlyWRGJw2p86SppmxavLNI6+hqR2tq2taCy9GJwhWijzVtESiEEuR5hpCe3bairGrSPOP80wc8+uxhgPqajt/9/VeU24rOhLQ4ITVGeqwG2wmshabu6FqL1IpUSl68+ZpyndHahmxbsbp0bE4zjh8+IfNpeN/SYK2jqjYsr+/YbFek41FATDpHaSUSQSo0lfdMPVgvcU6RJGNam2FEQjJfcHR2xvHnn3B4ekqm0+AGaAx2u0MmilmRMp9nFFqh+6wCvAgEQeMojaM1jqqu2axX3N3ecHt9xd3NFZvVHW21o65qmrYOx8EFZPBkcUyapyyODsgSRSIFRZ6SjQpQKabtMGWN9GHRRAqkliRZRiYUvqupq5qqrvBAlo+Yzhbk4wnGhHvHaDzCixA2lRYpp+MTcB8njwPA2BLhNNbKvQ4+TNchzOr3C/ahox6eIXSjcVIQCXjRiGjo1PvQIiEkWof7nJAaBvg6JBCGbc9Dv//h8HqeIeVPeBf2snfKi02rAJwXYeH292RCiMZEe1A39wRDoC8I+pd6Zzxphx2QeLyMWQia4PSzH+oWxhv3R0kMMdneh0VZ9cZM9OTI0J3vyTsjvPXO1o919goayZ5iokf9ojO0GD6viALJ/jX2x0R7Xf8e6O+5/7z3ZwE9h/LeyyF+KB/Y2/3tBwuCv/7rv2Y2m/Hw4cPBBOfVq1fDYiGEGBbuyWTCYrHgq6++4m/+5m84Pj5mt9vxzTffDBJB59xgVdx13WBwFEN8YoHgvef58+eMRiOMMe+MCWaz2YA4xFl5lBzG7jrC/ZGrEAl/xhgWiwWr1WogDUZ0ICIWm81mKFjKsiTPcz7//HOcc5yfnw/qgzj2iGFBcdYfsxDOzs5YLpd89dVXvHz5kizLOD09HcYK2+2Ws7OzId8gvrckSbi6uiJJEv7Lf/kvKKV4/vz5cMJH5CPaNeteGgNhXHJ3d0dUVUROQjiRwu/HUU0ssiJhME1TxuPxcALGUUckG+52uwGdidv3ORS+rzD4mDwCIS3T8YREazySprUY79nVFd5ZhGu5vr1gW24pyy11uUMKResbnFVMpwe07Ya67WeOzuFtw+HiEK0ky3JH09Zko4z5OGOkE25uG4rJmMZCnia9jbTupT+W9WZDVQcpYGs6ZoWkGKdU2xUKRT4Z8/DTx7x58Yb17Qpnwg2VRJClGuEy2rLE2sBRkUJhqpZ2u4N6hbeOmozr6xUvXr1G5yOyRLKrPVXdsSlL6ts7qstr/O0drbZc1kfsyg6HQwjHd29XfPXVa+azQ04enJIfHjE9OOXw5IzD01Pmx0ekoxHCgWkt1raMc83/n7k/e7bsuNI7wZ+77/HMd44RCAQGgmQmk0y2MiWVssusX1QPeiqzeqs2k5n+Lpn+hn5qWZt1y1pKSZnJzOQMggACCMR453vPuEd37wcfzgkkCarUpozatEAE7z1nnz0dX2t961vfN5yWjLKEgXJ8DYx1zo6dN7XpDZ22bOqe+WLB9eUpt9fnLOe3rJcrqvWaarOmairatkb3mnJYsLe/z3gyZjaacjzdZ7PasK7XqFQwGgzJ0wx66Oo1pu+piwKVZKCUs/G1AtVbKtOhNxW6rtFdT9e1tH2PRqHRIB1MvX94SJFIR+QTTvY4CMS8ja1pajfutwPFb9sITmHIQfD+53IbbbbB/k2FvvBaY5wr31YcyCMHvXFSz7Q77QXhUQQnzBOmDHbJbC7EblsM1kPWLvD5MWa7M6ochHX82wM6EQ7VkfG8dH6YqgiBOCrwaZSfWgsnHs7VWEtvpa+WJdY4xDRJJCpxATNNnOy9kilKJpHTwM4xuN3KmPzgz9ShGoKdfCX2+I1xI6FShvHKcHhBVGn7x+6MhIZzcKZS/n4JyS464RAc66+42PIIwg5CyyDcU7sVevp927cmBKESD/3kMDEQeADn5+exag2CP1dXV7G//9vf/jYK6wRDpMlkwmg04uLiIu6zrus3kABjDOPxmDzPY3UaJgqWyyVSyjiSF1T61ut17NuHEw5iQWGePyAZ77//PsfHxwghOD8/Z39/n+vra5RSse0BMJvNGAwGTCYTHj16xOeffx5bF03T8PXXX3P//v0ofnR7e8tqtWIwGPD+++8zn8+ZzWZx8iKMYC4WC16+fBk5CWHMr65rlFKcnJwwGo3427/9W/7+7/+ewWDgekbGMBwO6boujnwGnkYgItZ1zXA4ZLFYAETUY1fBMby/LMsY+Odzx3INrZ/Dw0OGwyFHR0dxqiI4Lv6u4P67OAa7bYa3hRToXmN6ge4tbW+QqsUgaPueVAnQBoWgtw2rds5qNSfpXPZutKRfL2hbV6kLAXmRM9mbkk8nXL66pO8M0khULxCbno1dYqwD5rPhEJulaAkqEaRIRG/pascTaOsGi6VbNyxuF0wP3iGRAqM1m9WGpm7QxvVpO92jcNMPpnfthyJNyKWlXt4i9Ybjo2NePztHDUd0QnG90MivzpAiZTybsVo1zG9X3Fxesby+pJ7fYLqW0b1j9mcTRoMldduhrSQtCyaHxxw/fMydR484uv+A4WyfLCtdH1gKMJpxnjMpCga5Ik2cloC1jrDZto6A2fWGtuvp6g3VeslysWB+u2C5mLO8vWK9XlJtNtR14ycWGhCW2cEedx7cYzKeoITAtD2262mrCisErdGM8iFWSNrO6UiAoUMgkhyjMqR1I3oYS11VKJnQ1BWy60iVBKvpdYexFpVIbC8YDXISqUAopDBIYUmspH+LOgTj4T6JzLx0rQ8IylW0NiAGETnYEvFCVS0IVaPdqSYlWtstnCxCMrBbjYb32/hz4StPi0v4he9SuN3sJP++xeBIudv61AV1z4z3s4VCuL9178iSfefe4YR5dsacQ0WM8DbQ20LcaHdObt1RMVlSiSJJU5TKUCoD65Ijx9HZ4Uf462BD2yCqG7rjNjGR8UhpvDvG6Q3Y3QTCH+VuUrHTFnD/CMiHS1j9D+O5SuVkk4V/jcEg9Lb1EDgT7sNMTJLC6/3uwAYOSWgj/f7n7FsTAq11DJThIAJjPUDLm83mDQOjwJgPQSfICoeJg9VqFR0CQ9UeRvRC1hNg7qDzHxKC8CeMBQohaNuWi4uLuH8gVsy7vbAw4hdkgcPxhp//3d/9HUBEFILiYSAiBgnl0CIRQkReREAWws9vb2/5+uuvmU6njEYjXr16FacjApNfCMHZ2RlhjDAkRbPZjNVqxWazYTQa8dFHH8XPCAjK0dERZ2dn8RzD+YaKv21bDg8P45RBaK8EnkAYWwwJSuAbhGsZ3BTTNCXPc46OjmKb4cWLF2+gMNsvwLdPFLythKAoC2xnsMKiraE3nVtUlZs6qOoOqwRWGBJpGaWKxEia1NIIg61AGYmxApEL0klOVTd0VzeI3pAnCWmekySSrulodEdDguo6Civo256WFuthU3rrEgGTU1Ub/z0WnH19SiIUaZHSrDdcnd/S1h0C16fsu4a+dhPoGEO7XpE0grzIyEyHNh3DwYRytGL/6ABhBLZp6Oqel1+/QJyesdlUrG+XbBZz6mqB7hvy4YwPfvhjhoMpi8vX9Ciy0YTx4RH7J/fYO7rPcDalLAYUWUaWJxSpYpBKBpmkzFy7QiAx2tJqS9e7JKBpvbfHck61XlJvHAKwWa9ZLxes10uW6xVVU7npCwRFlnF4dMJ4OmU4GTHd28MpuQtECX3Tsrq5RSRJlOkWwukYGClZdz1G5SQyJxGSvqnBExmlcpMT2gc4i+NUYLRDiwAlwPo1qRcKIwQKgbQC+fY6BhTFECmccE6oLsHPxweFPu9GuAXAPXHACi9o4xUfffQO8/yuGg+mRx4JiFmCJwyHcO+zBPeXiaZDxKAfIP1wDGKnUvW/t0FDwXoxIYPwrB5jnE2zMVszn7BDY7eoRqLSN9occpegGP/4/68CSVGC3WqrBFW/bfvFBPrkm8Q7P5Ms43XYHpbHTBDSvlHZs/N78MXRN2+qcPLM1hqCYBS+knfIgmt7EI7MJyMuBzA4hUmPkqB3EjevpeAPZVd3ILSSft/2rQlBYKmHwBA08tu2jb3rsiyjME6Y/Q//X0oZq9ugDNg0TRQ3CpX2biCXUsYqNLw+9Ox3K82maZjNZhhjOD09jchCWZaxPRECZAjUR0dHUUFQCKd9EPwWDg4OYhW+2/8Ox3d7e8v+/j5HR0dkWcZms4kQ+s3NTWxphEXq7OwsTjPstjIChD8YDFgul5HgFwiRQfXw888/5+TkhDzPWSwW8bzD+aRpGhOzkPSEaQtrbZRIDv3+MHoZkoTAOQjXKLggBk+FpmmiDsRoNIrqikEnIoxQhm1L9nlzJHH3929jk1LRS0dAUsIpf/V9g0U4kZDVkvVyQb3aIHvLKCsQwLrZoIYp0iikG7yHFGQqEa1GrypymdFKSHMn19pUNW3XuqVNt4hmg/QjWVpCr51fed87+2DhqzyjDaubJafyNWmRU28qNqs1IEnTzEunGrqmR+sO3Vb0m1saKckYkpUFSTbC9DWz2ZiDO/skKmUzX3B7/orz16+oe2fs028qdNdgMaSDEfsP3uHR9/+UrBwwub6HSArK6Yzh3j6D0ZRBOaAsMwolGSaKIvN/UkmmPLSsLW3XOSJl01E3DVW1Yb1asVwuqVdzmvWcqtpQVxVVVdNUG+p6Tas1VkCWpowHA/amMw4O90mHA5K8IEtTdOfcHIWQqCynMxaFZToe+nFShbTQtR19Y9DSYoVC9x1t12J1h04kRSbotVdzA/fZ1hcORoN1+2rrmqSoSLxdsAWktai3qEOQZDmC9I0qFC9RuxuihGfu2ZALhJ/5XrkQIdC60CeEiugCdhvwwky+63PvBvlvwtF+i/8OSYn1EsC79sgh3PoDsxaQzk/Bj0sqRFRKjC0RtucUijWXEARTJuUCvvQeCB7kCPuMR+2POY4dhrXqDYh9C8Lj2yMyEge3iVgIvPFueILjNtiGrr3jDthwM+KxvDn+Z4SJxYF7jVdDwsEvxmwTAieQ5DkXNhyLic+BfwS23IPQdQjJwX8vQhCg/hCwA/zb93203Q39b601q9UqJgnD4ZCyLGOQcT2brUNimAQI5kdhlDFNU5bLZfwc2HIBgEjW22w2HB8fMxqNWK1W8VjH43EM1iEQhomC+/fv8+GHHzKdTqMxUZgeuHv3bjzeMGYYyIh1XccK+d69eyilePnyJcFPIEwOrNfrSCAMLpC7QkBd13F6esrNzQ2PHz+OCVfXddy/f5+yLLm+vub29pavvvoq8gDm8zlFUZB7YZvb29voWRB4ArtJTEBLQgskODSGkcOyLCMvIyQ8oXUTkJnVahXfXxRFTKDG4zGr1SpOfHwz+Icv2TfVDL8tK/0fuVXryvUXE4VKnaoi/AAAyaRJREFUFX3fsZ4vkCKh6WsWF47EtmmWJAlkeU5vNevFmmEyQ2YJwmpEn2ClgaZnoFJs5zTChTSYzLgWQ+0kjIU2kNYknWSgBgiboE1C1zsOgUwkne0ZjQZkae4rIkO9aag2zr6414a+7+g6TZ6lWKtdUrlaopsFQmh0UlALicxGqOEQ218xbcdMRwOG0xnrQcL65muWt2c0XYf0Y1hSSbJywvTuI9753p8yO3nAaHbA4YMPEColSXMn2yohTyzTUrI3KBjnCZlf+LS1tF1P0xjqtqOqG0fU21RU6wXrxTW38znrzQbdtvTNhratqZuGumkwfY/EUJYDinLAeDBkfzJjfzYjH+as+9b1ez0M3OveTTYIgU5cIpXkzg44SRJsr9G1RliN0c48SXQdQbJVd9pXxU4LQhvj0Jve2z5Z3+sVirppkPWKMskRMqXHItG8xXwgsvQD21yEUCOEh5u3RLcIQ4vAat+tWEPV76txKR3W7oPltsL3TH4jcOz10EMnJg7W2K3d725NLdxInCPGmYjrS+kIq8GBMOzvjapeKRKlQAjniyC27Q+BcOOKSYITNVLbAO7h877vXMD06n0OWbDYoOL4DRTTBeuQFIZz2woiuc2NeoazFFvJH3893lQCDMnENjELrZhtXyLkQ0LH5on/CLFNnEIyExEMn7D417l9b70g/ECi4+7giqHdM3XH/+0P8R/089wd/dtsNvFn1lqm0ylnZ2dRTyCQ3EKAfP36NavVCiAG8aALcHl5GRn7aeqIV8fHx7FlAESL3jC6tre3x/vvv8/Tp095+fIlFxcXkSg4HDr9cSklh4eH0alxPp+zv79PVVWMx2Pu37/Pe++9F02RQi8+SPpeXV1xeXmJtTYaFf3kJz/h7t27TKdThBDMZrM4TljXNcfHx/ziF7+IAkwnJyccHh7yxRdfRIh/tVqR5znT6ZSbmxt+9rOfRfRjOp3y5MmT2FK5vr6m6zqur68juhJEogL6EBQKQ0Jxc3PDfD5nMBhE18WAuASEJ4gVBc7HZrOJLYaQAFRVFVsY4X2hZRSIlEEz4k1Ws3hDYCokKt/0QvjH3m6uLpjsDZFasG46lssVq6tbEqtY2ZrrswvMaoNF0ym4abS3xRVUq4rpDPJMQJqjMfTaUi9XTriob8hmQ+QgpWsatDYk2YC+MrRtxXBUMs4EDT1N2yBVTpIVpGmC7luKPGNQDkikQhtNkqbM50ucVJ9boLTuabVGdzWry0uaTUOSJQxmU/LplHI0I82H1H3LdLrHermhaxvKBIrRkPl4n4tixGw/JSlL+k4j0wHjo7scPfqQk/c+puscFyUtBnStRlkYJIq9ccFklDIsBLkAqS1919N2mqrTbNqOzbphvrjh9vaa1XxBtd7Q1hv6dkPT1lRdS1s37vr0HUb3IKAclNw9OeHw6BipMvpGk6kEpLNgTpMMhEKIjN62WOUJcNbS504ERtvOqUfqnr5pqTZrdK9Recri9pK98YgkS+l6aLuWddOQpymbtqNvNW3VslrVrOoaqwTFoEC3Fdo4FDTrO6TMXE8ZlwS9ra3arBEiIQZ+tr38EF1cruYB753vZYTALbEHLjxaFpwFrfES8gRUwX2u248G9LYvjmPB696R6XaK34BJY8FNK0j3WqnceKJSCamv8reQvqvwZXitlO54IgfBn3FcW1x/f1sx+4C6A6tjt62AbbXsji+GbV9BWwxSWqyQWInTUhBu0sB4CUgbKnBhnVWyN5QSAt+3Cc6CIl68gMwQUJmdpGu7JtpoiR6u4u5MgCOMeo6A/QYCa7w+g3+/a4P4KTAptz8PqIBvE/13twyCYE+o1nclbMGpFU6n09iTf+edd1BK8atf/Ypf/vKXUbq4qqrIB0jTNDoTBsOeo6OjOF73+vXrCIEHY55QmR8cHHB1dRWZ/EAcwQv/vn//Pl3X8d5771FVFU+ePKEsSx4+fMjJyQl3796Nc/rgiIOhYr+8vGRvb4/VasXV1VWUMQZ4+vQpt7e3fPzxx8xms+hl8Dd/8zcxcIZRxJubG6bTKV9//TVt28YAHdovk8mEs7OzmFSFqv/s7Cz6PUwmE/7pP/2nfPLJJ7x+/Zr5fE7bttGvIYgXhWsRkoWAeFRVxd27d2MLpCzLyPUIugRHR0fRI+Hw8JCqqjg/P4/WzOGhttbGCYT9/X3CFEhAGXbNkMIWWhUhIXhbScH0YMZmecNqfk3X1DRNy3qxYbOu6aVlOEopJwdoJJtmQ9OsKFRKU+ekCFLTgZUIDakBpUElCdmopLu6xXY9fS9otTPz2U/GVHnL7M4Rm/mKwnQYbelNS1YolMqxVmC1pGstapQyGA5o25b5fI7uLZmAViZkZUZqDf1qycXzF272/ugYUQxIswIhErqqQ7cryFLqpmJ8cIgxS15+8nPSruHe0SE/+p//73z3g8dUtuTzF+e8Or+m7i1JNqLxnAbTdYhBz3icMStyZkXGKBMk0mB7TdsZqqZlUzcs1xvm8yW31zcsby6o6pWTbG0a+rajazvqxpuM6Y6+25Dn0ut6nLC3f8R4MiXPUjTQ9pY0d2qHq7ZGWYkQCZPhkL61nt/hlsmm1yipmBQF7XpO17VRMjqVklY31Os5SZZhbY4QKVIosqwgyTPyrMSaAWtRY+oFbb/GWsGgLMnzhJaeUTkB3SCFBqGRMgPyt4ZyAdiuwop02yDwlbMJ82UBiXOvjrwK2PabrTe3ivvcRe4itC4ihB4CnhAqBk8BboxTKpI8QQnhA/oWtldSgZJu1C9g1dty1ynweU+GXmts1xD69+G4ZBi7i8vGm+vHbuANiUpsn+yg4qElENgRUoit0+Eb5L7eJ0TBQCPsU8bXBF2GcCWkb31Ya3njbTub0Y5foMQ2EQvHvPs4OUGnnfdhvUZD+MStSFywmg56A2gDfr3N0oQkSXee1TCFEi7RTovkd2zfmhB8+OGHrFar2DcOiUFY5CeTSSSfvXjxgufPn0elvYAohDG5EIBDzzr0pQOT3xjDxcVFtAwOFWcg733yySdRm//w8JCTk5OooPjd736XqqrIsoz9/X2++OKLCH+/8847zGYz/uzP/ixqDQQ0IHAD7t27x2q1ioGwruuo6b9erzk8PKQsS87Ozjg/P+fo6CiOTP74xz+OFsm7ffwgL5ymKV9++SU/+MEP4pTDfD6PxxhaGoGRH8ykzs/P+S//5b/EiYuu62LiEK4VEANx+HIHyeQQ+A8ODjg+Pubw8JC9vT0ODg6o65qXL19GdCU8aEBEPoKhU2gJBcGoMCkC8OrVq+j1EDwqdsWO3raxEcCri5fMr25oVmuUdYFFphIzAOqGmh6rLW3V03cdQhiMdvK/eeGgS20ltteuJdBb0tGIrrGM9yYwSGiFxgqBSTNq06KbipuzczJRcHPbQG6hKEmQ6M55DJRFCcKy2mzotPaz4Cmr5ZJH793lvaNDvvPBQ3LT8tknv+Y/ba5J9++iZEa1qbBSYJSkFaB1y1QV2DJnuVqjGCIG9ygzwezOIQeHjzi58w6bRrNYdtS3Ndd9RWs62ramrStubq85KQVFJhgmGYU02FazblqW6w03N7dcX11xO79mtVpSNxVt07qpgLamaSuapnV9/M4pvkkBRZ5x5+4JJ8fHjKcT8nJAkuUUWUmWJFRNi9Yb0iJBFArTKUf1s5b1cokVMrq4CWMRBmZFwWY5p+82aCzSpqg8JVUlptqgBOSJcuaOykO0tkP3hmXdI0XCdDxEmo6qylhXDoXrrOPNNF2P7RpUX6AykMrNpr/dTftKNHSMA4TtkblQKYe1wFqkUo7Rbx15LnwVhYcXErVVFQSigqCUaud3AilSH9y9aI50DP9oaui3AINrC0JbOt3sMPP9ix3GTxh/M5EI56cNdubmY1WN9Zw7G6V+7Tf6N7FN4s7EB+ot/O6SjDfRgSgGJJ1fiJvi613DYAct8TAAVruxx5AQBCtlrEFbFx+dx4fnZyDdcytAijB+uNsuCcHdxPU/ihR5UqQQvtJH+fuS+ERgd4TQTSD4k6bvt9yywJlwyWAgSfz+NflbE4IXL17EALFbYQrhzI2C1W6AiENvOfSZA6s9JBFAlNANxL71es10OuXu3bssFgtub2/5oz/6I7qu48WLF5FQV5Yl7777bhThASJxsWkajo6OInweNBGOj48pioKLiwvquo4CQrtKfOEzHz9+zOvXr6nrmtlsRt/3fPnll5GHcH5+ztXVFbPZjP39fQaDAY8ePeLly5exhXB6ehor/HC9Li4uODk54ezsjOvr66j+9+DBA+q65rPPPgPgu9/9LqPRKAbRMPe/G/BDgpQkCU3TkOd5HEMM17jve4bDIcPhkOPjYx49esRsNkMpFVs+gVw4GAyiDKcxTmkrjBmORiMWiwVVVbFer6NDZNB9uH//fvRHCKJT39QpeJvIQNgWNy/oa0sqBMMsJ08Ela6pNg2tMNTLFqRxi6GVWCNQ+YCT40OKIqHZLGm6DbawMJb0RjKe7CFMw2Z9TddbkiQnyweIVCHLAdWio51v6KwEkyB6hWgNnawhkxTDkulkRJoVaO3U0oyFvu1JVcr/7S/+jO9/8JBMCJ5+/QqR77H34APWmxq0kywOi4qSikwlJEIg04I+79Bth1aS3hjEzYLDyyua7h5ZrijGBdmgRNUdaIvte9rNipsXG8zmlsF7j+glrDaGzWLFzdUtZxfn3C6vqauKtqlp24a2a2nazuk6tC1d51oCQkBZpMymY6aTGWUxBiEYDIcU5RCVpPS9pRE9nTHUXYv0MtoShUgSurZ2C7GU9NZVWS4YOuMEYXrW6xVJAla5kcyk71FCkuWZ/774QkgohLTQdZi2w/QtWV7SVw3N8oYMzbQsyZRFS0uRF9TV3E2V+Hl0ADd3//Z0CHTbIWTKlkLmq/jYL3aCP1IlRGpgqAjx5kdS7VTzLug7dT2B8joGYqdM3Y74Sb+rUFnj1ABN0BIIjoNh89W6kBij409CS0P44+39iKEQoS1gMdpGNn9oRzjtA9931xY8o1/Eo9yOAgaTo9iCF34KwAf8KHgUXA4Rnj+iwDgKJEEgSYRWi29FePg/uBjGKQVjcC0Vn7iEHr/XEHAqjsqLGjpkw6kQSvLUjZImIvHPmxM0Mv4EpcAnQ16wiDDxF0yLAkfDIyEh8OOSGJ8y+s/+w+vxtyYEq9UqQsZ1XUf1u+D2dHJywmq1iiZF4XW3t7exN73LrAeiEuHDhw8juXC1WsWWQghEAVEIpLi7d+9y7949Li8vuXfvXrQADvbH4d93797l+Pg4jhMmfjzp+PiYwWAQTX1CUhJg+zAy+fjx44hQPH36lHv37nF1dcV6vebdd9/l+vqazz//nNlsxmQy4f79+2it+eu//uuozxC4AT/4wQ+4vLzk1atX3N7eMp/PI2kwEC131RSD6+J0Oo2yyEGAKXA5gMgfCGhH0F8I1Xw496ZpompiQB5C2yYkFkH5MKg+BtQlJDV1XbNcLpnP50wmk8hNKMuSw8PDOD0SfBrcdzDAYjtZ6ltCCqSxKG3JhcS0G6qqwWhN0kFlFGnqErfxeICQCcYqhsMpaZHRrSqSTJAlqZsGQqGNJcsknUkpxIyhkiT5EFQCWPIsR90Zsb6cc3Z2jmuZWxRuEbWdYJjvU2bO9CopErIsRynJ+fkFJ5MhpYVqWfP17ZLPn19yvjGkeUnaO+BT5alr4Rkn2GJSxbrZkJgMJSVauSBqekNFx+18xWbTcLA3oVCKNFPIBETdI/ueZrOmtYYygfXlOa/nl7TVhuV8ye186cm/a+q+p+la+s4lHV3X03c9whqyNGE0HDAaFIxGJcPhgDTNQSp6a+mMS3oSlaCkY12b3rk2WitcsEpzpFB0UtJ3jV8YwZLgRgQN1mpMu6FtGxCJI9RJi277CA8L5Sh3bauB3gcVhe0twhiq9ZLbyyuWq5WfUZekJKSJYlAMMFkLBvJUkig8Q1ztFsP/6FvXaa9BoLwInl87pHIeAcpB9cr35QPZLELT1mKlEzAKvQC5gw4gdlwHPebuv80IEfgHECSGEYKgmmysRb5BfDNeRGfX24Q3IGtrLUlqQ5aw7YvvFhEhObHbqhzcxEcI/7vJQGyXoLfwAv4zPUJiA4wRNhEEfeQWiQjrltkeKyJoJ7x5bcIUgZRZyIPi9RXStVMciTLxCY7/IwUCV/FvPQqccVLMKoQzW4uyzj4pDkmb9UnU9truXGJvjx0QIxPaL9Ha+Xdv35oQ7C7yQBxdCyNpYQRuOBzGgFUURWTPB5fEAGeHdsKjR4948OBBrFjX6zWnp6cxsN3c3ERC3GAwYG9vj/v37zMcDmmahvF4zMnJSTyWYMKUpilHR0cEkmCYSBiPxxweHnJwcBCDNhDdFANPYjwexzHJ6XTKo0ePODg4YDQaxdbBq1evOD8/5+nTp+zv73Pv3j3eeecdvvrqK7766qvI2t+ddAgMfyCaGgU9hEAWDOTLkEQNh0Pu37/PF198EdsR4TqG0clnz54RCJe7ds9d10VzqOBCOR6P4/jm7tTIrv/DLkchIBBhdLGqquiVEJKsMLEQ9vVNFazIcn6LSMFmVTNMpwzyFGpL0zhp4VYrsnJEORxx5/5dJrMRQkq0FmRJRrVZYfMUIQbYlQGVoIoUYzv6pqEcH5ApCV2FSDJkOSDNC/IkZTCaUE/3aCXMr5cuEKZeArU3WM94F2iKNGVYpkilqMuCD+8c8/LJ11Trhpc3C17dzGmNJityhBJ0vTeq6hP6vnNjdFqjwRvIKFKRkaY5ptdYrak6y+2q5nB/z5kB5S74SeGCed/VGN1SreH8tEcYTb2pWK83rDcbKq/q12hN23fovkd4v/vBsGCYCcYDN35cDkoGZYlSKdqVpd7FzenXWwPa9EgroO+wAlTmiKtKOiKXEO47o41xTGkfAISVIBStVLR9hxCWNE/djL1Hx6zu6bTACoWyGkTrZu+NcYTIpkbrlkZ3mAB9S0kmMgfyeqhZIVDW6RdaHFNdf4ND9Y+5JWmGVJkPMD4oSYFUCRDY5YqgWhiDUgheYgcwgBjoTAiQAvROwCLsNfSfQ0AJULrfT6jiw89DgN+O6hHfH1ACCAp+4QfmjRaAa3m4fzvHZT9VEX4WK2YLYVLfIwFgfAIT1h3ftvSEPN17F5PgOeCfTSH09hwDuhDcIhF+VNF/Pgohk8jFUkqRyCQenAhWxT4xc5ctBH3CThxfIU4nWI/ESH+lXEKA8KOUbzwNofDySIC3Rw8EwlB8Cf/zYL2MRw++2W554zn7vb/ZuTlu5yJC8sFZcD6fx2AUVAellBG63iWcBYb9bDZjOp3GajZI7wa1vNC3Hw6H0bnv6Ogo+gDs7+9HEmPoyWutOTo6iiTFqqqYTqcxuGdZRpIkzGazNwJU2H9odZycnMSJitFoxIcffhgRiPv37xPIjk3TRPW+R48esb+/z+PHj3nx4kW8Dk3T8OmnnzKdTikKZ5UZqvgkSSLyEVoEQXshMP739vbY29uLY5/hegShp9lsxueffx4RhXC9QxIUXCI3m40jd/mxwqBqGFCBXTGjMAIadA5CYhWufZiiCDLNu94PsQcm3nQ//OZz9I+9rZYtxX6CthJBglAloizIVMn08IR8ULB3cuIMgJR0VUTXs1mvUMOStMjpGw1KkRYZvbZ0TcdoeoeySOnXF848ZzBhMDkgSRTDImc8mdIgSMoLmrpFGEO7qTGNc6HMxwNEnqB7CTajyFIe3jnmg3fu8JvffMEXn3/J+WpDIwTpoESIhDxLqZuWtu3cfL5OaX27x1jr5VY1WZYzGI+wxrKeL+mM5HZdY5EUecqgyCnyjE3VeyMXjTUdm82S3kv/tk1LUzdUdUWjO+gsvdEY4WDVLFUUg5Lj4wMKOoZFjkgSP7KYYbWgx6BUQqKUq+OMW5C10Y7HYTXWChJAWjB9T68NwhgypWiNq+4NPQgPJdsELZTnXQhSEgS+RWA0wlr63qKldOhD3yGsUzHsO82mqhBoZJaSANY6ueJM5XSNs3TWxoC2dFVDIhJUprDGWSC/rS3N3HUVMdi7gLGrRqiNJtDnpPDjdGzth12g2yYKFi+p6+JqxPVDwJU+QCG8rkAMxMSgIsMkQNiv1x2I1Svb1oN7Y6jj/b7Cu98IlsJbbQsfDEVMEHbjkc95fHD1ELmAoHpojZuodKqjnqcQpyWsr8jdc+gUqWyE9R3ykjrBI5VgbO+kj5FIkaKSzGmEKOUkkIVyaIFPBBA7bo2hleBvW2hDAM6USfi2RfQwCC0Sg5CJT5C21454TbeQQEwIrEVKJz+OMD7pcMmXFF4hwm4R+29ufzAh2N3Coh8SgqCyF6SNQ987tAJCtZmmKdPplHv37sWxtmCgEyreO3fuRPnfoKQ3mUw4PDxkOp3GwBWCZAhaATafTqdRFyBUy0F6N6jzBdJbqJRDhmeMoaoq7ty5E183mUwiynBxccEHH3zA3/3d39G2bay2X7x4EQPobDbj4cOHLJdLFosFq9WKX/ziF/zFX/wF1lqOjo4iIjIajVBKMZvNOD09jboCo9HI695LlsslV1dXcRpjNpsxHA6Zz+ecn59HxOHw8JCjo6No8rQb5JMkiQlKuDfBKyIkd0FtMYyM7iZy1lrSNGV/f5+yLHn27Fl0fAwJ1XA4pCiKeDy7X9pvPjtvY9M6ZbFsWJiGLJWUoxHD/X1O9k7YPzzAWncdNm3n0aMCbROywRSRJiRSgEixtgepEZViMJkxmExJsoQkA7Qly0YM8jFIUElKWmS8+/6I8f4Rq8WK1XzB1atTmk3Ner0huV1gRyVoQ55m3Dk65PGDuzw4nlKOh/w//p//H64Wa/LxhGJYYK0hEc5kRkiJShLIUhiUSKWo2pa+M3TVijJL2dubOjKcce5uVdXSdYYiyxiVJWWekyYNTWci9Ny2rUMB284lHk1L29Z0GJQ2pEnCsCzIy4KiyJmMRjw4OeHq7JS+0+RZjpAKrV0v2AroLGRSQa/pmtb1ubMMmeZYBKl0BLCudTLNxjr/B6UEyhq63plHOaU3iek17bpxa6FwvW3d91hcdZwkgsRYOhTaKKyu0X2N7hp0b9DGwb9W+MpPOza+ShN66VoiQkm0TlkuGmy/YTyWWKNZnF+8lWcY8C0r4/X4hVcldAHOvQB8RHd+DE5LF1cN+8BrQ9yXMbjoaA0gXVa2BQKw1vl/xIQAQsnug3Fg7HsRIKkAhxRL4bQiADCOUCekh+VN4EMF1r7XRdiphHeTDHCoTSzfd6SMXYWPD8AWcIRW4YOjO2fpSXf44O6UAe3Oa0Jy4sTCEpI0Q/nkNs0LksST+kTiPSVCv999ZphOMEFOANfuCmforqA7/qg7IPA+EP4Gej2JrSKiQchgwuSPzwa3S98+8ZmSJCRHbu9SGqQwKGXJMkWWJKTS6yl8S3H2rQnBN0fJQkIQFOuCbkCQMN5VrxPC2Q6/++673Llzh8lkEscYv/76a9brdazo8zyPJMWyLNnf32cymXB8fExZlnGfWmuurq6iy2JRFJGkWNc1i8WC4+NjNpsNSikWi0U0Fdrf34/qfkFUKbQO8jyP5MNdZcYAo//gBz/g888/J0kSHj9+zMHBAcYYXrx4wddff83HH3/MwcEB7777Ls+ePYtKjlVV8R//43/k8ePHnJ2d0XVdJPhdX1/z9OnT6LEQoScP6b9+/Rogjvu5L4WNIk1Pnz4lSRL29vb46KOPIqciiA2dnp5GH4jd0c+QoIVrGdo6u0ZRAVEIdsiBLDgcDqPt83K5pCxLiqKIo5ohEdiVun7bxEJBQlu3TPcn3H30gMM7J6RZQZbkNH1D32qUsd7GVGClQuQZo6PSZedAMT3C2B7dbtDNhsFwjBYZIskwmRt5S2SKSpNYXSmZoixMJmPKomQyHlEMC77+4gndzQK9WLFpDdmeRA9bFoslbXPI/e98QH6v5gfLFb/57Zcslht002J7Q0NP1TbOh8EEddCUNFUIkWIKybxeYkyHNg0yycmLjDxJEDKl6XuKXFIWKWWekkhBY3uUUMg0odo01HVDVdc0bYNte0RfkyYuYDx48IDRcIS10PQdBkvbNmitWNcVWklyLElSeGSlZbNekCR7CCnoPGyfSoVS2pvIJDRt4/rintVupaW3HV3XgNEkQiGEQveaar2i3tyS565y62vorSbNHYLT6gatLda2YDrWm1s6XWOkCwyKjKauScsSkedYkTh+gxYIlVNmBbk2tF1L12TU0iEXAoMo8rf2HPe6c2qNqG3PWJjYPohRHFwhHCtoF7RjxQoEYsHuJBeYLUwvcAGTDqyTbXZiN9YnYhADS/zcwGQXPuBoTO+TBxxnxCHWAbHYcf/zUxBhs9Y4uN0jji54W19FG2eXHBUUHRIQbI6Vkr73D8ZIMP5iKYGQmXMwFAHCl/EYhErI0oxEpR4ZcLoJeJIf1h2LQXr/hh6ne6RxssuBjOlZDdafh79Z0m5TAveaHdlkB9cA3lpaAMY4fMZfd219m1cohHUIkJLu8BJpSVPl14KUrHB6M4lyyVYQ5+o7Q9N09N3/Hy2D7U2yEfofDodkWRYDyjdV69xD5Ubgnjx5wu3tLe+//z4//OEP+c53vsOvfvUr/ut//a9RFjdU68+ePSNJEj766CM+/PDDNyrbQFx88OABo9EouhJOJpM3xI2stTx8+DAS8eq6jpX5bDbj8vIyVvjBTChUzUHUJ1TZwe3w/Pyc4+Nj/vW//tdRuGi1WkVuwXw+5+7duwgh4qRB0zQxUFZVFbUIrq+vmc/nUf735OQkmjCFBCc4QVZVRZqmPHz4kPF4zGKx4Orqivv376OU4vXr15yfn3NwcMAHH3zA/fv3AXj+/Hn0SQjXMAR7Y0zUKQjtn9BSCUlJQFtgq0Wx2WyYTqfs7e1FZcmgOhkEpna1C8Lf4Vq+tU0JUpVw5913uPPOQ4YjB6UrYNOs0cKQpe7Y27pGG8FgOEEpB9VpbUEmCCNJ8pTh8ICsyKmWNfRu4ZOpQCUOD9TGk36sUyxMjBv/SscjytGA8WzCV599htlsGOSSUkGzqnnRXfHg6Ii1EVyuW1otGJVD6AxN2yLShLrpUAgSKZxHu7XYuqPetG5UTilyVdDWLRevbyhHI8bjCSfHRwxTSY8lSWCQpwzz1DkgVholoa0b6rqladw9bdZLdF0xkJAoSz4omaSSVFhaC0VeMJvtuUUvFUxHU/IiIVMJQih63aFNS29aqvWSJCtAQKokKkmpe+j6ljLtQWukkJjeLVwiVeR54kYXpUSIFGslXdNSb9akiXbsdZViZIZMUkwi6LuOvllTNT2dsfT0pEgSlaCFxCiJUgWTg0PysqTXlrZzYlNGa/q6QlmD7TsOphOu5kvqvsMqRQpMpsM/8LD9j9tWqyVKZii5tePdgm67MPr2T0CYZehpE+cFIq8sBCxnjRww7Z0xQYRP4KKqge9ZOx8CV6GriN1H6N+/PiTI4WCjSVJkx/t/e6U+42OINpZWeza/daZVIr5bxnOK/XfrOABWOzlmKRVJku5U82ksVBB4Tsu2vYnypD4DvfFiTXTELCkiJP69PvGRISmJbYjtKGBAaBzNMWAeNtb3QVsDu3s5PA8C4z5bCpJEkSlBkkiKUjEe54yGBXmWYb3vQdtpmrajrlsWi4q+s/SdU1A0xqUsxrcWvq08+4Nuh6EvHKSFx+MxQjgDH/egrqJB0e4WxHNCRXx6esq///f/nv/8n/8z/+yf/TP+9b/+1/z617+O7YQAQ3/11Vfcv3+f1WrFxcUFbdsymUzY29vjwYMH7O3txX538A8In3Vzc8Ph4WEcpwsPwMXFBcfHx2itYxsgGCYFZUUgKgUGyByIbYqzszOUUtEyebFY8NVXX/Gb3/yG58+f86Mf/YjDw0M+/PBDFosFk8mEo6MjPvnkE0ajUbRDPj8/j2OQYWojJEQnJyekaRpFkOq65uzsLKIet7e3rNfrOBmgtebOnTu888473Lt3j7Ismc/nMbiHc9idAlgsFiyXS7Isi9MCQRxqMBhQVc4PPsgzB1QicCdC0hBUDnedKkO1EdCB8Ay9TYSgSEve++AD3v3wA/Jy6EaEhHaOekDfaTrdkvk2WFvXQMKoHJIlCb0EUkd2Q2uk1VTVKnZHBQKMU08T0i9KgNa+r40A6aZrSpWSFyW17hkXOUnXszw/5fryHKFy6v5DJIJuU9PWLVhJXgxQac56U6GyFJMqTG3oPHysLFhPqrba0BlNUgwYjmbsHR6zf3LMdDIgaypm4z2GqmWdtRR5SpGnKAVVtaKta3qtQVq61YLbFy9YLxZM9qfcPzogsYbl9RXj2R7ZYEiSpeTa0mjIVEK1cdfNpk4XoWobEgtDkWIsaGtRaYaVyn/fBEZaem2RVkPXO70BY+htgtGtJyZqhHGjjW29xpoWgaHrNNauyUyFBjo/vmZaTd20rFZLyjKlTRNUmpMVI0ajGUU5pfMiUnmekqREgx+JZTOfs1rccrvZUPl5blCgcsqy+Ed/fsOWZTlKpmCVf8ZMrEBD6+SbiFxAc40Qse0iBJHkKaXYGW0OD9FWyyBsvQqyyMF2N3APvFqfwesDbMl01hfNpncjfNY7+4ViOHxGmOMXXocgIBhOldKjzfiKPjLzQ3Df9txFTGocCTLM8Ie4vNuydMZJJiZUbojAEwetR0twdbw2PY5jo31PH49YuO891iKswUrtZJlD0hVlkK1vZGhPUnRJhBCgBaSJIkkE5SChyAqyvCDLcj9RIv0UwhZJMdZxYa6v1+iupmsFvXG6Hcbi/wiMdQ6vNnAWPCrhJKV//3P2BxGC3Qo6MOgD1GSt/QcmN+Hgw0heGGFbrVbc3NzE8bsf//jH/OhHP4os//CePM+5vb0lz/NYye8SCYOo0d7eXhyDC4ZGd+/ejTP2gR0fphuCCVKSJFxfX0eBnUCSCz338XjMcrmMExS7EwwBkQgExe9+97u8fv2aqqp48eIFZVly//79OIIZPvu9996LfIGbmxvWa0dOCnLC4AiaFxcXdJ3rZQeC32AwiJbLxhiyLOP29paPPvqIruuiaFJAQqqqiklNkiQRNVmv17ENEM4rJEJCiHhdQ8tk976GxCm0M4LdcxCqCihReP1uMrD799vYJsMhB3fvolSKNRrd93R949Tv8gyzqX2/U6FkgrbQm54ejUSS5QW91vS9Nx9RCtNpjNVu0cGxvMEvwF1PZztUr1ivXdKRKEHaW0QiyPOM+wd3EFjqakE2mTDoDaa3ZLkiFbDWhh7pNfhbOt3R2x6V5ejOkaKs9pMKwiccSqDSHNvBZP+Ag+O7TPePGUzGZKViPBpghSJTGQrpKvy9PToS6k5zcXGBsD2jwRDVNDQXlyzrC+qmYb7a0JgW48fI9qRCqpyrqys32z3IyMZDbxjUQt8jTY9KUjfKKRNEkiKUG5kzQG+cBGzdNKSid5MAUriRQWk9e94gjCaRgs62aNOiJC4wbjqqZsnG9A5etcKhDAag4PXTF/Si4e7j9zk82SMfTsnzMakqMLoFJHXjEjiX5EqSJKUYWDabiqbvKIoSes+GV4LGtP/wAftH2oqiQImMAE3HcTvPB5K+Ko0Wu+Cd9Nz/c+tRqKzZSdyNpx34HjVht8Zr/MvQTYv9/fgi8PtS3kjIv9C67wnWYkL0EW6fxgQbYeGV+EILQfnCU3rpXeWP373WTZ4kPhlR7BIbwwdY4fUWsF7/IKDWuyqOvm0Rz8dB9saTEK1vTzhgIxDwwpRCv+3vW4E1bg/G9FihEd5aWgmn65AoSZoo0ixFqjDN4AmHUkUkAyxp5sS1rJQ0fY9pez9R5IsRE9ZSx/swCLR2pGATuBA7oT9wNdwIqED4ds0fWom/NSEIs+phlC0EurD9vuovy7IYZMJkQehxh0D05MkTptMpH3/8MYeHh6y8M1oY0WuaJgbAwPQP5kdhf7PZjNlsFqvdNE0jITFMO4QxvqurK4BogKSUommaSI4D4ux+URRvTEi0bctsNotBNrQv8tzJmd7c3DAcDqPD4NHREScnJ5yennL//n2ePXvGZrNhMBhwcnLCfD6P2XY4pkBEDIqAu1LRuwE3TEHs7e1RVVV0aVwulzFhW6/XMRkBohaEM//o36jkwxjkZrOJCVFICEICFEZMb25u2Gw20fxosVjEdozZqSq++Vy8zYTgzjvvcHDvLipVdF1Dp10CI4VwZKEs8yWCIstzkixnXdWApe07R5iSCoxjBGsLRodAokjTZKsEpq2D8YVgU1X0WiKMoO80DS21aCgnQ6R1C4BOU9KyYDCbkQvFdDp2XAaBmxxzXrVIBFmaoaUiSxRZkjt3PoQbirOGQimGoxFlusfhyT3Gs0Py0Yh8UDIcpOwVkkmmaZdX1G1Hp0GI1GkFKEtWDJADxXQyRjWatHzpEv+up7WOVNhXLTfnV2gNe3cS0qzE6I5yMHL9+6Z2KmkCEixJ4sfi0gKRZOBHsLRxSU1Xd2TK0gvjGNCeAGalINGaNJPQW2zX0lUburpCCuMBVUPVVEgEiUxj37YcjSnaAfOLa4qDguFkj+H4gCQp0UbStB3abPvUxnhXVQt978Ycy8kU1WXoekPfb9B1S98b6rfY+YqV3k5UDmh2lPkNL4xY/XZiAP+j6JAX+AVeZCeo+Dni2ja5CPPsYYxwdz/beXqDZlutYwUK6d+XuMpYCLzEEyFBCYQ+IbbHI4V7+KXcRRqd6ZRUykHunkhorZc7tts1xlXyIfnQ/vwsVnvI3HhUg8Bv8EmFCeN6vur35MnQmpHSoIQmVZJUKa+noZCJQqUFKk2RUngPgdDjl6hk26rptSO1aiMwVmKMcM+ytqwrwabZCh0Z42Sdg8gWJhgkiYi0aNtjcW6q7PAhiOfncQ7r72lEYH7/c/YHWwbAG0p437a4hyATZH9DDzwQCuu6jgqFRVHw+vVrxuMxjx49ipV5CGRBGCf8CUJI0+mUyWRCURQRIQj7DBoFYbY+OBaGKYLJZBIDbhiVDO8NegABHt+F36y10SY5y7IYlIPhUpAf3rVVDqqKDx484Fe/+lVsa4SqOlgoP336NMLxocIOrpGh5RHsnMOoZkiqDg8PmUwm8fyC/kPgQ4SEJyQWIckJidKuD0RABPb29iKvI4xszmazaIW8XC65ublhsVhEQ6Ugl/zNdkF4ht5mQrB/fMxoNKTVHU1jIgMaY0lkQl6UGO2UvGSSkaU5Xae94ZBBdx1Wud62Ndrl3tqhR4qtRnlvHJSXCoU0CcvbDSIp6BtD01Z0uiKViqnVDPOcPE/J0hyd92AUwyxnsjelw5IqiWg32L4GDGmauFYCihpD4hNRawy677BdjxAJxWhEeXDE3v4RWTkgLQrKYcF0kHM8EpwUlstmhZEZra6oqpamapxaoFTk5Yg8G7BAonECSLrvSIoUmSjaVlNvVtTa0CvJ4ckJUqYkMqFQGSqHVjnbYaREWoNMJDJPEVJhDBjjZIgToOs7eivR0k19SQlSWlKLY6b7x6bvOtpqg25rkjx1yUSvsUYhk5SsKBFSYYWgHE4xm55q03Lv40fs7R9TDMboztL1bkZdqhQrbHSF09rQ9T297cnzgmIwJG8Lrm8rrk+vWN1eI4oUMZi9nYcY6NoWLVziGipbV6iHJCFUiNYjR1vRs/CyUCnH6l76wG+Nk4AOoSugCiK80usKCJ8whLgj3WtjK4Igv6uQMvGom4rkwK1WifTqfi4T2CYtb64VjlAYWg2uTRHinMuB5E5wC4iF3KIHwk88GINVIKzFRlMi3zbwCY8KeZTcJiM+N0FJSJVBCUOmhIP5VYJKlHu+0xSpnOooniVgcVoYWPworaDXPX2v0cZiDA559ElCr4PUu/atknAt/JikvxmRAwFY0cXjF2J7r0P7Bht4H/jr6NGEb9GI+4MJQVjs/5Auvdy56YGhH8bfQoB6/Pgxo9EIKZ3RycXFBS9evEAIwfe+9z2m0ylVVXF4eBhFkPI8Z39/n4ODg+hLEJjyAcYOwj5CiJgQhMC5yxWYzWZsNpv43gDZh6rfWhtFlmDbex+NRrFvH3r3gUh3eHgYIf1wrcK5HhwcROJgGBcMsr+z2Yzz83NevXoV9RtCRR+q+OC5EHr30+mU6XQapxDef//9iGAsl8tot1yWJaPRKAbnMGYYpgWCzHTbttHNMHxmVVUcHx8zm80IFtWTyYSPPvqIu3fvcn19zdXVVWwDzefzSM4MYlL/Z0oIZJpg+5a+beh8myNLvLytEGRFAVZgtFsS+04jUaQqIVHQtx29dgJAAkiERFhNkeZ0fU/XaIRKsMbzNNIcmo5mXdMKzWq5ptos6XRDlqZ0QpAe7WONQcmMLDEYZUEkDKYzamvJkgSzuqW6uUDLhLwcIBBkiaIyGpUmqDTFhNaUFsh8QDrdY3Z8hzwvyYqMcpAzGRYcFgn3h5ZJJlmUY6ysqJob5vMlN7fXrDZr8tGEFEG1WDK/uqZpagaTMYv5DcNhiRSCal6jO009X9Domt5sODl5RN+2GBWcMRM6qUAl9F2NkAKldoKPsSTCIJTApIJN3XvilBMDSqwkkwlC+HYk1gVr3YHpSWzCurE0vSFVQ7I8IS9L0rRAkWFbyenZGZ0QHD54RDnaQ8gUIzqMcCJeMtkayUgShHC9YtO7ajJBYmtYvF7w/POvubh6QXE4ZXzv7UkXd22DU11UW2g/sNECOU0QSXDCV6tRG4RQ3GyDvHMtDD1zi/UaAtsgHVrAlqDIR0z8JUK5wCN9AqB8200liVPmk8n2tR5NCMmLDRoH2zNxx+eljt9YMfyIpbFEuF5rYlIhQvATzuVPyTCJsYX+pUc5wt8O3veSxgIS/2yo4LwY0A/h3pNKA1bjhv7C4KCzWO61pao1VdPRtdpB+lZhrKQ3bhRR2e2MAda1IozRaN2jrRsN1rr3qEaQe3aVffApCPdYhOsgnAmUUgKCsBLuvlvhhMDi3RNb8yj935sQADGI/KFFPfTMpZRRWKhtW87Pz9Fa8+DBA/74j/+Y73//+5ydnZEkCV988UWsRD///HP+6I/+KCIMgR2f5zl37tzhgw8+iCZI3xS8CQE6KASGSYGiKDg8PHxDijeMHUopox7ALiu+KIo39hvU+4QQMdEJMstlWSKl5OTkhF19gxCkjTE8efKEuq75/PPPefHiBZeXlxGtSJKEP//zP+fm5oaLi4uoy5DnOX3fs1qtojZCXdcxQQoISgjG1tp4TEDkZJRlGV8fBIuWy2XkPey6FYZzDPc6tESC/HFd1zx48IAPPviA6+tr2rbl6uoqBvzdFkVIQN4UEfkWJsv/wK0yNauqidm40RaVQFYUrOuaYjikzJxzpe57emtAuWsQVdh8VWWspTc9aZrQ9b2bsDAWlaQkaUaSF64HWK04Phjx5MUly5sLtG6RmaI1lkZbOplStR1KCNqup+4NNpGYdEBjJEVWYNqG68sLqlpTFgUCy97hESUKlWTUbeNMZBCkwwEHDx5y8OAReZaTZSnDYcl0mHNYSO6PBQeF4vRizadPzvns6Stee2+N1eqGHkMup1jTcfrVU86ePUWgufvOA5Y/v2FQ5GRWYKlpsBjdU80rzvqKeycPSDOFKBJkkpBaZz6DNUilMDgkLUkTrLF0wtJrjVSWcpTRCkPVaGzvp+USBSp1SVjfU2YZnZ4jcQIu63XN+aaGIiVXjqypSJE6o6skt6dzXrx+TjJWfqQwQUl3TYzqSdOMqqoZDAonCiPxgStxkwm1obutePXlMy5On9PRMLl7wNE7j7hZdm/lGQbA1AjccW7XK7fIC7Ml0MWkQLtxM5W4AAcWpCS0o91knsEY94NQEQdeikQihZsukEq44C9dz1v5/rdj+6uIRri2g3P3c7HfABJrNdoS4XorXGIIgc0fFPR8HInJoyMkuv1KrPbVPK5NEcSZAJf84LkgqUv4klSQpoo0UeRJSpYkFHlGmqWx0vaUB6xRLjhrJ47VG0Pfa7pOYwzoRqC15/Vo6wiwQqNN79tgCUJkRH1kv19t3LXocetJjKNCxUQuwRl0JYlXVvTWzlKCtTq2DqL9MR7t0AYjLF7byCdUvm0hpE9wpGs/Ir2ctCL5Fp+uP8ghgP/2HnCaphweHrK/v8/HH38cYfvhcBh73c+fP38jCKVpSl3XXF9f89VXX/HP//k/5+HDh1GWOIzHnZ+fR7Jh4CIEP4EwV392dhbJi4HwmKYpd+/ejfD5LokxwOWhOldqWwGEIBeCfkBHgu5B4DqMx2OOjo4A4lhgkBpumoabmxv+43/8jwDs7e1xeHjIfD7nF7/4BUmS8Mtf/pIsyzg8POThw4f0fR8Nk37729+yXC6j/PLBwQGDwYDT01Pee+89ptMpQojI8g9GUnVdR1Lm3bt347H0fR/RA6119FTo+z4aUYUKPyBCQSTpJz/5CYvFgsFgEMmbYZ9hxHE3Afjmft4WSjBUgvn8CplkTnAkc62ATdVQlANSmSAFKCXptaDRPbrvaWzDIHOCJKa3kfUrXL5Ar3uSrEC3HUiFVCmZzNC65er6mnbdc7FYsllXSKspRM5wOmZ2cI/HP/gR1fqMy2dPef36NU1vuffOmJdXG9K9GYkwKCl5/4P3WbU9z54/56vPPmf06jmj0ZD84JBkOEFlJeVoytH9dzm+/w5ZmpGlJdNJyV6pOB5K7o4SJonl5emGv/zpV3z+/ClnF+fM57fU9RqRwGQ0pigLJIauWtDXK9JcMZyMUUnG86+ecTCbUPVO50IK55Sne0NdLdDKYDKJTRKEtmAEfd8hkpwyz0BJjO7o2w5jLGmSkuUpVkjGgyGLdUtVbeh6h+JssEyLlDwrkX1Hv1iyuL2i6ntEUTI8mKGM6yMPyoJBMWEzbzl99ZKXr1/yvf/5/8L5pwdcXK2Z7m0oglOfdMS00WiI0T2lT/77tqNpOjqtKUn5/JefcnrznJv1DbZI2btzzL333uNe9fZIhXnqbJyFUL5Nb8EqrPHEwWhKtG3dJWrra7AtL6XnqQhvZuSiovQuels3Qb8G4gOKlD5hEFv4Hp/s+11bq52iX+++677Yd/3sXetlEciOyicfGiE0icTdI28FrLJA9lQkiZO2ztKELHHkQicWFI7P4sR7wv5N5AE47pShbmuqpqHz7aOuM3TaBfy+7Ylohd1yNqzVsQUQjKSsk4D0v3d/K9H4JGiL3oTmTB+Lph0uhm8LIH1CgyNPgsJaueWMAFJoP4HjUFw3oeGVDeOy6q6VTJ1vgitkHP8g3gdp3ed8y/Z/SKnw9227rYI7d+7wZ3/2ZxwcHPD8+XPKsuThw4c8fvyY/f19iqLg6dOnsX8doP7333+fjz/+mNlsxnK55MWLF2/Y6wYRo6IoYs/+/PyczWYTA9ezZ884OzvDWsve3t4b7YPnz59zcHAQhX4mkwnj8Ti2L0ajEVmWRX5CQALSNOX4+DieZ2Duh0Rmt/oNnIbQari8vIxqjZ988gmvXr1CKcXNzQ1nZ2e8//77/OAHP+AnP/kJt7e3sRUSEg6tdbxWxphokhS4A0FQKfApmqZhtVrF+5BlWTRu2g3Qy+WSPM+jm2HTNKzX66iYGBK70HYIycLr16+5d+9enJgIDohB8GkXLYC33y4ASIsJpjPkSY5KEpquYV1VjAduxt/Su3ln4eRhEwRNXbkRQw+5DgYFFoHWhrZu2VQ1aaooy4LReII2hrbrqOoNujesW8HLF5cYqTG6ZjO/Zvm6wq4f0BUn6Nowmh7SHGzYVD2bTYe1Kc9Pzzl+55CRtBSHJ1y8PEenJQ+/86fIw0eoskBZSIXAaMFwus/hvfscnJwgE0GZKKazAXsDwcOh4Kh0ffzPXyz4y5894fNnL7g8f81mfYuxPYNh6keJp/S9omoMe4dHmK6mKBR37t7j0ySh6nqy6T5J1tC1TVxspdE0dUdZjEjUwOntZxKVG1TfeutdgdFOCjlViiLLMFbQtj0yMaArlLAI02K62pPYnDPdYn7D2cvntPWGJE8Yj4YUeUmvFOVkjGlbhtmIxUXLs+evuW0W/NN/9S8YlnscqD3+8q/+C+eT15RlznQ4oTeg+46+79xEgnLJatO2tG2DbTTPnj7h2YtP6OwSIVLkYML4wQk/+tGP+fL/9fO39hxrZCCau56xh+9lmiIICJ8P6L6Sx/eegwy5je0AT5wLI4Rxmj6Q+xyU7u6zG6eTvodtI3TtA5ogkl8d+Q8IrZlsa9nr+u6CJBVkeUBSvYCXTEj8pIL1SUevQxD0hlamw2hN1xqaxqA7B9Vr47RCtPGIgk+QtkWIb5V4JUQIlbvxY4ZOA0EIx+FyKo5O0jh4ZxhjMaJBOqMLgpKAC8gS7a8TNvwmiPNtRyAtKvI43O+2ts8iikIFbYXwJ1gsOxMzawNS4J0VjfE24zsoNyIicoGkubVUhm+TLYY/kBA8fvyY8/PzKEUMby7y4SDCaGKwHP7444955513ePDgAYvFIhL4NptN1OF//vx5dEUM7ocHBwecnp7yt3/7tzx58oTz8/NI9Lu5uQGIlXwg0wVDJaUUt7e3kRx3cXERRxKFEMznc66uriJpMAgszWYzHjx4wBdffBH3F8h0o9GI6XTK2dkZJycnHBwcxJZAMEnarYZ3YfEgpvT+++/z5MkTPvnkk6h8GFoDu8ZFu94Cbdvy/PnzaBy1v78fDZGCm+FqteKzzz6L1+/g4IC9vb1IZgyOj6GVERCTpmniNEFAWzabDWVZRsQhjGAGbkSYUgjHOZ/PCZbUk8kkIg1hkuH/LBoEAL/8m793M/BJQpI5NnrX90zKMcNiRJqnyDxBqhSlEgc9Ckei6lVPV7tWk0XQNj21bz8MioI8ydFGo4QlkdBZg9ZwdnbBYn7F4eGY6dEYdbekXm/YbFq0rXj17Bkf/+lHDCYz9o4M2aJCa0FTGZbrDUL06HQCI0HbO6/EyWxIU23odA2J5OD4kKOTO0z29klSN2EzGRUcDhUPxoq9TNI1Hc9Pb/nb3z7ls+cvOT17ha6WdPWCosw43Juxt7dP2wo2bY+0UIzGyDRjs1oxv7xmOBlxs1kjhiOm+8c06zVtvabvavqqYrleMFBuEcUoZJIgpcb2LaZz1zopFGXuzJmEylAyxfQttu9JpCRJDEhDU62Yz2+57Q3P65qNrsiGBdlkhhBOPKhtOoQsWW80AzJefX3B+cU5apLx8Q8/YjKcImXK8M4Be9M9NosV68WcUTFAG6i7luEgp2kr1rVGkmB76Fc9V8+f88Vv/w4KKMeHDGRKKxSb0zmf/n9/yq9/8p+Bf/N2HmSRbu2LpRuxxLPelPBtgcAZEl6oVzh7bE3vA40NDWg/vucCv5I2Bg4pBU6gT3qUQaLAW27jkAWfSIBBKIUkJZohhFE9ITDsrokiJjF9B03Tea2Oyiv++UDuJweMtdH/Ilbsvjp2nLYwcmcj4mAjxwB3rjt9eytwvfhIqvQ9dRHWqpTAn/A/2CanwiJJiHoJYTMm0DDRPvnasvu3RE4nra09imLin8DdEAI/FuhvGiJyPcIUBYQxY59IuPjulBltSOICN8LroVjp7o/a5UNseRu/a/vWhODHP/4xVVVFf4EgQhQqyiDaE+Dux48fc3Jywv7+flTq29vbi0EsCAK9evWKX/7yl+R5zr1797h//z6DwYCf/vSn/PznP+fs7Izz83MuLy/j6CEQx+BCxgsu8C4WC4AoqgNEdb1AIFwsFvE4gGhsFObz1+s1dV3HFkVoFwT55O9973scHh5GsaXhcPgGOhD+3nX9C+6JH374Ia9eveLrr7+OkxQHBwcsFovoA+EeIBuDd3BDDIhDCLjgErCzszN+/etfs9ls2Nvb4+DgIAoMjUYjTk9P4/GGRCMgF0HwKEwrbDabyJUILZXgYrjbCgjtmHAOAS0JHgvhdb+LfPq2OARPPvuUoJvuMnaXcacqIVeZg0kjmcj9rbKULM0oipwsyxmNx6R57ujGQpCWuWP3Y7DSOkgzczP363pJ0xmywZC7Dx9yeDSlSAVSd5i252bdcfXyKav3HjDeP0RmJelgQVdVdK2mrXsqZehVghwMEa2hqRraekO1WpHlKeO9A/bv3GE0m5EWric6LQccjBIeThSzTLBe1Hz9+oZPv3rFk2cvuTh7jWk3HBxOub6oKDPJwXTA8dGM0/M1lexcgChSRJazvr6mO7tg//5dbj57wrCcMhiMSLMCIaYkaG7OnWLn9dnXjCbHyGzM5XzF+atTbi8v0K2GNGUwHjAZjZgdzBjt75GVqZvhNtDXK9bzG85ev+T0/IzFakOS5ui+R5UpgzQjyRSkCShPhVMpXd9zdn7N2elrRkdTTh7eY392QKpSBuMJTWN4550HfPn0CafPX5AoxfTgwClVdj1ZOUCKBGUFdbVkfnbG6YsvIRMMj/Y5ePchdw9OuHl+ycsnzzltvmJ0+HaeYXBWzE51T8XFPcD7SiqEEqjEBX3hbY2VUiSe6JckiSPLeatk6YNDQAR2N+MTeecMiBPeMZbeQ9Wdl9w1WnvVvoboOkgIZN6u15EGtoHWV+xOi2Br42utQxocr8HsANu+0R9e6Al2xuiIeOCpc+6jpAcuwnuED6QgrfSBWIAJwdtvUm3bDB5RIPx/KbB260ogPLHPHb+Kx7bVRfDHg8CFbRPe4JAK4dsOUePYd1TYCeyBR+HxiKDzENogHuRxP0fGey79+xACYxzS5q75VihNiN8f9r81ITg9PY0w+nQ6jWz2q6urONr38ccf8/jxYx48eBCD0cHBAVrryMwPJjvX19e8fPmSZ8+ecX19zTvvvMPBwQFlWfL69Wv+6q/+ip/97GdROyAQ5rTWUWhIShl1BHYJhaGt8E2tBNjO4e9W8yGpaJomJgt37tzh9PQ0JghBoCgo8wWr40ePHsXz/2ZCELagAhgMlj744INYbYeEIMz0h5YAEFsMoZoPo4TBnyEYEF1cXHB9fc16vWa1WnF5eRmVJMN1n06nHBwckKZpPL6u61gsFnRdFwWXwnRAaAEEyeegs7B73MEfIRAfwznsjmj+ruD/thKCVeukOz1QCITMX6PonHUuftnx/5HKVUGZlKRJSpHnTjgkSUizlGIwpBwM2N8/oBgMGI6GFIMBmVIUmWI6GlAc7HFwuMdsNKBMJLppOZ9fU62WyL7j5uyCd48/pJiMGUzHVPNbLp+doasOXQj6tqfdbKg2NZvVmmazptUw3Z8xPTigHE9ICufHMRpkHA5THowFe5lguVjz5ctbPvv6nC9fnHJ+eUHfbJiOh+wfHKDbGmUbikQwG5XMbztuxcYtgkKQDwfkkwkylewd7TN8dcloOCLPcpJUkueKIpHQCc4vak5ffM10PKfrEy6uFszXa4rhgGKcorG0/Yabm5r57Q356TnTw0NmB1NMV1PdXHH+4muen77gdrnEIhmPZ1gpMdYtxL0xGKGwMgELCsn64obL09ek44SjByfsHR2hZEbfatqmRZGwd7DP4OVz1rdzLs/PGEyHpFmKESDJQEO9XHH9+ozL01dsTEV5dMDJ4/d494OPGLQJG7tCaI3Mej74J99/K88wwNHhAUpmO8z9betAhGpcbitf9/sdop/t3Sw7gl7v9P7BB1dPNLSuKncGVU4N00lyBxth3xt/IwCGALz94/YXJhNkbFVERX9rPcpBCHnRn8DuVNnh7yj5K/xrjHE/CUTI8NK49oe0xP/C+uo89F18II79/h1Uc4sibJUTjf+Zu5ZhzfCvs2BDfz9wAxyxA8GWz+BAhNC2sB5h2E5vqJDEJUFGGrdW4ZCeeB2ibYIXkwpWy56k6FAW1+I0cV028d5+MwHc3b41Ifjbv/3bOCZ39+7d2GfP85z1es3h4SH/0//0P/Hee+85WNX37g8ODtzOvRxsMEO6vLzk008/jVMGQUHv2bNnfPXVV/zyl7/kxYsXkeEeAtSuOFGY/Q8kwdBzD4S4YOYToPIwUhdaByE4FkXBYDBgNBrFscKHDx9iraUsyxhoAyLy6aefRgGf0FbY5SF8c342VNDB7jm4Eu6SDsN+iqKIEwKB4Biq967rYu8/jGyu12sWi0V8f9CJWK1WXF1dRZEm1/8eRB5FqPYvLy9jwA/iTuHeBmGioNAYRhHDiGK47vP5nM1mw3w+j8cSvlDfDP5vs3VgyyPcimG2i4b/YxCezGO34kJOgQhrO9amQ9YVYr1GWIPEoqQgkQlZljMdTplOpkwmU0azCeVkiAUmmWJ2sE8CtKs5Xd1ye73kycszRtOM/WHOZn6NtIbJdMJ4NmJVJlw9e8bmdsHweEpXVWxurljOl6w3G7SxpJMZo9mM4WhC4kdmR4Oco1HG3aHkpIDVquHLF5d89uyCp68vuby5pm4qxqOS/b198qxkPJpimgXC9JSps2sWwkHNWEs5HDu530HJeFhwfP+KvMhJhCLJEvIiJRMwHE4YVgdcX89Z385ZXK9YNR3Do0Pe//73mExmtJ1huVizmS+4en3O1cU5F2fn3Ht0HyUNN6+ec/rsKy6XN7RYRsMxo0yiZUpL4mstr2lvBFZDW1dcvz6nFw3vPn6Xo5NjsnRA1xrH85gvGZVDkiLj8OSYi/NTNosFq+XCO78NMbVheXvL9atXXJy+4nZzg9ofsffuO7z33e9xZ3TEq58/YXN7y2iac3L3gPsffvxWnmGA6d4UQYoQqY97Pvgb7aY3jMZE5TovbmMt1gd2bXXsreugAeCDmdZ9DJ/WGyAY/wNhXbVqfSCGnWQkcBFCsI9JRmhfhEC9/b3bS5AODglCaH/4zxB45UWPLsQEQng+ZGgR7HyZZRiVdJoiNpTd7ohd8RiuSezjiy3z34JA7kD0oYjxKYwxO7j+NjkICQPGoRZubHAruhSTlHio/uJZEMI4dAfr1/3Eu/gmO5W+9S0BEfcjpSe3o3w6oTyi4NMp7YiQKOVaLmZ7PtZakL9/fPZbE4JAVPvyyy959uxZNNH57ne/y+npKd/5znf4wQ9+EAOalJJ79+5FUZxQdQY4/vb2lqurK7788ksePXrEb37zG37xi1/ECvjs7CxWwkGIpyiKWPGWZRkr4KZpEEIwm804Pj7mxYsXsW8fAnmA2kejUSTRhYQmJAv37t1jf3+fi4sLnjx5wnq95uDggDt37nB5ecnFxUVsRZyfn3N9fc2rV6948eIFH3/8MR9//DGDweB3IgVCiNjX//TTT7m8vIzTEgEZyLIs+jAEl8HgThhQjaZp3uAYLJfLSGgM9sNBjyGQDENiExKv3SSm6zpub29ZLpdcX18zHA4jSTEYPAUiYkBgQiskTCe8fPmSqqoiMgNbIs/vQgPeVkJgyP1iIX2lIt04j3KEIYRDR9yogUMH3PdWYBPtdMdNjzAaqztM39DVSzarFVenLxEvviaxlkwlZKVLMAeTGV0F7Sin2Vxx8eo5i9tb9u4cMRjeZ/9on8x2yHqD6DtknlMOBkwmA86eP+Pk5Adov7ho09P1PVom7I2nZMWQNC0pipLpOOd4mHBvCHcH0KxbfvH5Kc/PTrmer6g3K7pqxWgw4N0H96mqNXXTgFAkiXN+y6RwzoeJRLQa0Vuyckg5PWAwGlEA9x6/72bAk4Q0LxFSUrcNVpUcHN3h5kJT1SuWVQXCUGaGgbAoJKPpMfnAUDyA5v4Vz7/4gl9/8lu+fv6Mg+Njrl//hma1wioLiUD0GwY0NF2LHUzpm4pymLvEzWjaRvPq6SsW/ZKP/uQDDo5PKLMMrEE7ZSN67YxetICTdx9SDgpuLi5YvL5gtrdPIqBaLHn9+W84ff2M2jakRzOm9+7w0R//MY/vP+LsZ19y/fIJuVpy550jDu4/ZnX29qQKz65vwaYY7aBgawLrvPdqi4ERH4Kga5EFTw3hs+Gg/IeHoKVwCFqIVbGvHnF8izA7roD+P1JsA3dIDlzsC1yBZKdA96OFsRL3mID0bQZv6BO8EfABMqIOfu2QAoRfb2JLwlsGI7eQesQWfI4RUAFtNNraHcElPy6JRFhX78vd9TsEZXDcgXhZgi6AC9ZGa7DCK3CaHZRBxPMxwo3hSu/J4M7HTU4I3ESIk20W8bytT4B67dUIcdW9Ng7pddoLiVP5lDLaS7uxSJw4mMxQaYoSiVN7lGEM9Xdv35oQ/Jt/82+4vb3lq6++4ssvv+TVq1e8evWKzz77jP/9f//f+Wf/7J/RdR03NzdMp1Pefffd6IYXZvzDeOFgMIjjhW3b8tvf/pb1ek3TNGw2G1arlbtIvkIO0HmwOhZCcO/ePUajEZ9++ukbPgZhbl4pxcOHD6NMcdM0aK159913ub29ZX9/PyIdgWQXeAbgBIHG4zHBcTD00e/fv48QztDp5uaGm5sbXrx4EUco//RP//QN7kB8oDxKcXh4yD/5J/+Eq6srzs7OomnT4eFh9HsIHgVVVcVRzTABEJwKwxhnONbZbBaTl5D0BERglxMQiJaDwYD9/X3u3LkDwIsXL6IMcdu2DIfDNxQLj46O2NvbI0kSjDGcn5/z5MkTXr9+jTHmDS+G3XPflUYOW0ga/rE3u1qAkNjEE2xkAjJ1PcNEY5V0FQpuEXXmMQpEghQpQklInVyZVIpESRJhSYxG64pNvaRe3LKaLxDLFYvbK6z5Aqt+QV5OSNKC4bDk5MH7vPfR+2TTkuF4yGq9Ybm4JTvcI0lzDDlH9+7w8uefokXOvQ8+oi+GtM9fUGnjLFnzHFGWjGYTjiYD7owk94eS4yKhqlr++pcv+Omnn3M4SZgOMsxsSELL3sExaRr8MTqkVGgDVdXRNh1F5iSRTV/TGoOWKVYkGJG6c89TskFKkaRONbBzwUANC6q1Jj24g2hWDGTC5YvnPP/iOSezIx5+pyQZ1Ags3WpJs5lTlpIHD/a4+tUTnnxRUZaKtBxhvOCSzAbcdIKBSjFtTcuabJ2Qi4y6bnnx5TOurk75/p/9EakaO90A0WOFoOo7WmOQncUMBSJxFraz40NSIXn17CXPvnjOnekBrz/7LU+/+g1rDMXxMScPHvH9P/kjvnf/+3z2n37Cs9/+jI/ev8vg4D02KiGfTEi6t2d/vFg7UyeBcoktAmyCJfHW0c4aOCSzUrggp31QlvH76Nt6fr9WOLnoEIbisFxoCQhfXu7k89tmA/FdxKr8zUAakhDnE6D9O7wRjw7CQq6ad/4DjgOhMXFEL8grg3MIVDYkIaHlgH8/Ho1w32cX6sNhWKzQET0ILAWXCEi078drv0MBSBN3jsZLIpudgsds26RahyRgB+jw/X2EINmJD2L3NYjIA7DGma21beeug3JTF25aZ6eoEsJNV/QtfV8jpSJNUlTi2ppJkpPEUdsEpbYCS/GDf8/2rQnBv/23/5Z333032hFvNhuePXuGMYb9/f0YlAPsHcxxtNbM53Mmk0kcSxNCsFqtePXqVYSgAwQfdAFCANtl3Yf+dXDye/HiBUFPAIis+SzLYnvh4cOHMdkI8HlQKby6uoqOgW3bcnl5yd27d2M1HoJikiQxuQlcg+CDEMiHv/71r2nblvv373Pnzp3fG/SklDx+/Jh/+S//JV3XRaGh4N8QSI/W2ihpHCD9cD1DlR9m/7Ms4+DgIDpBBgOn0OII92UXaQmTGUdHR/yLf/Ev+OSTT/jiiy+4ubmJnI3hcIi1ltvb20hutNZydnYWxx6ttTFBCe2FXWJk+Dv8+20lAwDm+lNfHvlqSCggcf3otMAkGWQZUmZIlSLTDJFkyCxH9BkqTRBKQOKqEKsUbZJhZYEqhoyHh0wODMa2nnm/obuds3r9kl44ed7qpmG1XDG/XlDkKYaUd/7kR9x5uGK/apBDixaCbDhltV7TtJpsMuHd7044evc9bi6uOD+/RKcDpncOOdzPORnCUW4ZCMN8vuGvP33B3/3qt5y+esnCdmRK0GcJDHKavmLT1Ohe0+ue1WZDXzdIo7i+XTCe7TNIU6R1Pc2+btA95GlGJ5XT8i8SetPRrxtM1yJSiRoNKEYDkg0kWcakHJCpnMsXz/nJX/01RjegLcPhlE275nazZr7esFptGA0ytDBYRnT9ratOtWFZ1Sxb53cvVM/DD94ny0Zcnt8yv7wG0fD9H35A9+qUpTYM0odkiSHLSoZZSUGGKNyI3mKxRChJnmcUezPG8w0//+tf8vUkRXW3fPePP6BOC5KDQx5//7u8f3iXz/79X/L69W/4s3/yEbYYQjlmko64vG54fvnyrT3HWIUQW+Ev2K3KbQyCAafe8np3CIhyi36597lpBCVUfGsIWtoEXhYE6+Ftki+2hDYfgGOQFO7nkd1vwnhiSDT8BIJfb0OP27XUHTSvfUXs5vydHmBoAThug31jfXFOiluFQ2vdOSnh9BAd6hFGAXEGWr6frsPvBHTWOxbuBO2I8mO33hG+rx8kmRHScTi8f8NOyAdc+0V5/QjhURXpYXvj0QshvY+Bg0zc/fOS0tY4kalwHx26LYAEpSxKpaRp5uKs9FqK1r028BZDQ8ElX/8QwQ3btyYEl5eX3Nzc8Mknn0TC2sHBAY8ePYpBPkD8YSY+BNrQLghEtJcvX/L8+fPIUg/2xbv+CKGyDOTB0PPYtSN+77334lid1jrqFDx69Iiqqtjf34+GO/v7+5Rlye3tLbPZLKITXdfFlkEYtVNKcXJywmKxiGS+5XLJyckJm80mTiQcHBywWq14/fo1bdvy6aef8h/+w3/gf/1f/1cGg4F/6P6hsmOapjx+/Jh/9a/+Ffv7+/z1X/91dHJ87733EEJEW+cw0x+SmhCAjTFcXV1xcXER1RZDuyIkNIF4GNQMQ4tkPB6zt7cX2xTHx8ecnJywt7fHr371K169esVms4lJgDGG169fA0SS4Tc9FQKSs9su2TVgCvf2rY4eqh15VCExYVxLWYyqkLJHmg6oMbhgbXWG7FNGZUmRlAgrsR2EJcII0EJh0xyR5Ng0QyQFSZqTlTPMZB+Zl7TXl5iqQuieTrc8f/6EMGr06vwliV4y3htzMtmj3xg6bajajnGe0a9uSJISspw6HzAeTVH5lL3RkLTv6BpNLSSLVnO7WHPTGKresjI1YlCyP5tQFrkLFsaQGJxpUD7ASkGjQPcNp2eXjKcziiwlSRSi7WnXa5rNLWZdM3h4D7va0CrF6YsXvPrqKV3Xsn/vDg8+/sgpbuY5Uli6DvaOj5Ba8/S3c3772VPK0RDdX6BSgUozUpkxygRre8HBXsLleY1WGUmmkcqNUeVpSodlcnDMXrLH+ctbqs2S8Szl7v37pNmQdnJItVnRVJVTnpM5Umk6bUnTnMXNDSpxCV6almA1UiaofsWz5xf88R9/l5P7DygHR7Qmpfrskl/+4iWb+VP+r//8T1iKFDEb05uE26s5ry6vWKxfv7XHOBG5R60EYXQwjGJug8/OQh+w/ThuSODRuQFBFxl3EgTh9xmEhzLw47e7m8VX5hEO/2b48+6FUmGNQSZJJMa5Q/FrvXSJgbT/MEC5fr3jQgQio/uFD/BWY7X2fADf6hDGJQURPbFeNRDfFokqSf4/nvlv/YEJ6+ckxM7riMmK9SPVQoazsQjhTKGkEqAS0mQbTreCUA4RCdyH0HgRIrg2Svq+8zoDzuPAGsercA6tAiM0SSIQvr0pZVCF9SOHMdnzbRbhhaOEmygRgcIg3rxTv/M5+7ZfhqCy2WzibPpgMOCHP/wh3//+9zk6OqIoCrIseyNABA5AgPI3mw2Xl5c8e/aM1WrFer3+BxVluIgBGQgjgkEvYG9vDyAeR7Dx3SX1FUXBeDzGWhvFg5RSPHjwgDRNYy98OBxGjsFgMIjz82GaIQp5+OAYAm0Q+xFCxFn89XodCYdBDwHeTApCwMyyjHfffTcaPn399des12v+5m/+hvV6Hf8EfkTQKwjJWKjKg3ZCQAbm83mciAiJVDBLClyA4D0QzJnquubi4iImT0F5sa7rOKkR7mH4dziXQPIMLaGQXIXX7k4lvG2lwvzgke9TCt9nTEApJ0/ss3spVHDWiWpkQkq0SmiRqNhrdCIuiQSLQZkG1bXouqduWpZ1j0wyiuGAlJQuG5DlBUJ3dJslpqtgswGrWd9eMT8/o16uWN0sePXimqOZRGUpOlP0XUORZmir6aoVpjUMBpJM9LStJSkLlHWI0XzdsGl7rFKMR0Nm+3sURYGwYLremzc5TfRMue+qGgywLVzdzHmwWZPnKUWesvaeDev5nNNnLzB1g9UVeqN5/uQT1os5GoU5T5jcv8N0NnELjwAlMqyUyLt36HTPk89+y5995ztsNhW6aairmq5r0UYj0wRjIM3AaImSAikMCsmwHKCFYJKPOPvqFcvNhr2TKSf3jxiOpiTJAJW21H1HKyVaZWiRorVgUzWUIkHlOU2zAaOp5xuqqzmvX36JLGCqDjg9u+H9BxtkM+fqasnl7Tnv3jvgT//kY6o8Q5Vjbm4qbi9vWC/nDpJe12/lGQYQuD61jVUoBEJfgLhddbz9ngUi4G4IiMQ+34sPjIPftcndyp+APQSRG/e5FoGzBfaB2e9bsK3cI9KtA4HPoxh2y4IPI4BRolfYqMrnP8hpEHiCXxhNDCN6cexbBTTQmyyJwAvI4nEr5QWcIkcBNz1ot9fGXSuiAZY1oE0XkQ6nidDH/y9CUmMdr8DJCTvkwCEv/c69dKiKMT2BIKlU4v9IsC6hEjJxQ40+kXDJoEvelPScqLDHnWTO5TAuWTI6TDIE/sPvecD89q0JwV/8xV/EnnJg7k+nUwaDQaxgB4NBhOpD4Aw3NrQFTk9Pef78eRQ5CgHnmz3m3fG3Xe0CpRSj0Yj5fM7x8XF0LAzvD0EvMORD1R/Y94PBgKqqmM1mALzzzjsIIaL7YtiCpW84tkCoCyOUwaMgTDfsWi4vl0v29/ffkD/+XeS64K9wcnLC3//93/PZZ5/x7Nmz2EYJvIfwWYHtXxRFvC5hXPD4+Dh+ZqjUdx0TQ9B2vWP38zzPub6+5ubmhpcvX3J1dcVyuYzHtysu9LuCeGjPhPPcfd3uuYfz7/vw0L+dhCD4rHs7Q4RQDk6zuP4ezojHkQr9IqJCNp44P3cl3RhcGOMKEGIgFgkJPVg6mqpGih6sQRjr/ekzZDYkGbTotoLOoPuerq7pm4ZqueTi9Wv2hwcUecqghEttkVZg24bNzTX12jCdTKmXS2azEYNMkiSWVSW4WGrmq4qq2TCdTiizDKstnXErnZBu0TRNQ9d26L4HC70VrOuOm8WCwWiP0aBg1XSkSmJ0z83lBc8EPHj3kK7rqddLZCpRSYkFqs0amSYkVmDaxi2vSlEMRuzducvg5SnzqzmHD+6yvJ5T171ju6uE4XRGXW3I8p626sH6uyEUwgoyIWlubmnaiuH+mOHBhHQwRGaF04vQinw4Ih2OkcUILTK6VtMbZ1utAaMVzbJic3XD7dU58/qW7HDGO5N9lOl4/fIV5/YSKWBvInnw4BA5nqFVys31krOzOav5DX27prPQ1m9PurjvahAJgcEfesHCV8MuqIo3im0bXyc8VO31B9gGUOuikX/HtjoOhbS1Oyx3EQbtfGXtCXbWB3AhPDzve/zhvQFFCNwE7FbWd7sshJSGGAAd0hCCtptNEALMN3vxWNfPB4zVMcZLIQjj/qGtYbRr48VPCJ+/I8UcEwLpg7AFg8QY/cZcP+yQIK2v0ENA9tCFdCpPCOWI7CGhkl5R0pqtXoPylsrxWlt/P3e4CSJcphhb3PEFQiPCJQHOGnonYWLnnn7Lc/atCUEQtQkyutZaxuNx5AMIIaIt7m51Hy5s0zRcXV3x9ddf8/TpU+bzuWNHfmMLmU0I/qG633UtDL+bTCbM53PG4zF939P3feQyTKdT6rpmOp2SJEns9QeyXUADjo6OIvFulyMwmUze0DLYnb0P8rwhyQg3tSxL6rpmsVjE3v43t2+iIGHcL5ge3dzcvFFJB45ESDjC6F+Y92+ahq7r2N/fjzLGgasR7lVd10wmk4gchDHKMHZ4e3sbrYu/mQB8W/AO9wGInIXdSYMt5CjjfXubbQO9uXbfBJ9xO1GOALsKrHQa/Ajhk4EEqxKQCTbJIUkwNsEkjkugvFIcUmKFCDIpiDQnzwtEXYNSCKPJFfF7o1SBsCPMeonVzrYUYxBGo7uWulqDOqAYZAyVRngVNKt7mmpJt+lpN2s2C4Xam6CURGNZdZLbSrJcrbGmZVBOUXiNdiuddoISTtBcOCthG9A7bemRXM3nTPcPGBcFt0nlVekEXddwdXnOg4f7GONFcRJFWg5IshLT9SgpSVF0tI7CJUGkimI04d4773D6/AXH7zxEZTlJUZD6iQ6ZJQiVoGzDqloilCLPFZlXXqM33Jyfkh6MGR9OSIuCXluP9Dji2Xg8pZzMyIsRphfQNiiVOOEXYzGVYXlxzc3VKZtmgRhlTO/f4/0PPmTY9Pz8P/0Vi8U5s/0hD997j70791jaAmXg1YtzbhZLjGkRWDojsetvsYn7H7x1fYNToJPE+XtPStuid/7FIdhH6k5wJQymRV6PxQQA28TK00dcl3PsoPWx+sQHFB9dXMVvfJAOn/fNQshrCARE3q8HVrofbt37QrKNb19onJWRi7BCuQRcRwlfu5OU+CQFjyrYIABk4zFZi0tIzXYuP/AmYmYQTkKEvMsjF9ZB7w6B8Ofk0QcH8MsoIWx9q0aqhDRNUEmGygrSxMUy6YN/mqYumOtt8A6/t9YGRQmM7j0axDaL2h6mmzDRYHDn5mScvUCVCBnR9p592/atCcGvfvWrKOEbxvvu37/PaDSK42yhmg6Z1S7TfLPZcHFxwdOnT3n27FnsUcfH5hsVdKh0QwW8OyLY930URwqQepAQDsQ6IPbxR6MR4/E4zvzPZjMuLi7eqLDDOGOY6z8+Po7/PzD8gxBQ8EvY3QKcP5/Pmc/n9H3Pt23hfIui4N133+V73/sef/mXfxmTjd2sPVzToij+gSxw+HdVVTx9+jRyIgLHI5x/eG9VVczn8yhmFDQPdsWE/pC64O4xwTbQBT2F3UQhbKGV8/v2/4+xdZszjGdnK5milDM5MmLLXDYewhS+feDmelPICpKiRGQFIitQ+QBVDpBZ5qYPVOJHmAxSZJTlmGImfL7RI22H7mq6rkbXDUYYdDagq5dIoZHCkijIC0UxGlCWBYNBTjNfIE1LmliyTJClUNmO25sbiuGIrodGCyptuakFlRa01ZLZqEQp1+JAgLZh8ZFo61wHrXDfM216hE5QWcntfIUQliJzC5hUMqKRve4xdYVBoVRB02zIc810NCRLUsKqmiVpJE71xn32vXff5dVXT7k6uyQdDUmHQ2SegYCm75lM9umouLypycsB40lBJgVd20PXc3Nzw3haOiJZ02NsB0UHUpLkOcPxlLQYorISk7ig0jcSoQTtpmNzecn12QvWZk06Ldi/e8KD9z7kw4++h35+w92DZ3SbJV1TsWo0KzmgMyW3l6dcX9/QmxaVS2SWk+uS5edXb+UZBjAmoKp+yiB8NQ0eUdyp+MN6vENEc4HFB3RffRoCgVDE1sNu1e7m3X2y/wZKYOO/wVfyXi3PVeIhbQhxwWzFfKRzODRWE1QIhJ8DFLjvYuRJ7KAHInym33Uw9jUBqfONcutNlCzeWC04BPokQAdJwHCy8Ri3HIOoUwAx6Bvj/tZ6pzL3m7UeJbRhvFkhk5Q0Uw5RkxkqGSAS5xQppVeX9J4Tab5FPLYn7dBFB/uH9roXhjImjkcK8H4O/Rvn6tCMLek0/A92EsffsX1rQvC//C//yxtktdFoxMnJSQykQohoBhQW/91g1XVdVDYMc/a78HKAuIUQkai2+/cuwW1/fx9rLdfX17F3H/gEQrh5/9vbW/b29uLYXiA75nnOixcvqKqK9957740qPAgUBYJeIBaGNkDbtnz/+9/n8vKSsiz52c9+FlX+Qs8+yzKur69/J/rhbsCbdyBYL//5n/85f/mXf8lvf/vbqB4Y+u/h2ILccAi+YV8hgQjtACFEtDS+e/cufd/z9ddfx6QioA1ZlsW2xDd5At/cdomdISEK5MddBCCgOLvJ4H8r4vA/enPa5x7otG4xFF6uWABGO1KhtdqJi9jQ85OIXtFWCjfalaHSEl2OSMZT8skUkRZIby8b9pcElEwlCDkgK2bkpqNvV3TpDe36Gr1IkVpCbxgouHMwZv3+AyZ5QY3kb3/2a2aHR5SlJRMpDw7GLC8WLJc37NUHbExPD2xqw8WyZt0u6NZzTu4fU3UdSerMZPre0DQ1y3UDwo3/huluIxI3QYHk9uIFdVW7uWjvKif9IqytZrO8Rq+WNF3LanFNkRgm5R2KvREKZxhE35GVrm0otCXJcjoheP+7H/PJT3/B+3/yR2SjkiRPnK9Bb7mYL8kGLrHIipJsOKRIJYW2dHVNPpmyOp+zKm/JT3JEIam7Btn13Ln/DoPhlE3VYWyHSBR5lpAJRTtfcf7llzx//QQtIN+bML1/l3fe/4A/+fCP2Xx1yad/9dc8vDvkcPR9Xl3dcHpT85CU65dnrFenZElDYhQqGSDVkOq65dOf/81be451b0AohB+fC2FLCIFQnpDNtkp3Yje+et2tdm1wIAWscbC1DVUw2z464bsjtoQ1Dz+76jno4lsXAKVAWOlbBe77s9228rvOOMgFLPwEQ4Tu/TLhRYmJZERwdr/GcxJwcL0xOjr/BTGmoHMQAQP/X6f0Z0mSwAkwsep2Qj4h4QE3hrxzQAiU2iYswXdhm1AkpIlTrlVJRpLmpFlBkhUMhkPyvCRJt1MG4R5ZY7ftmW82+IWIOZEMH+6FuuQW4Nwmcf6JiOmK9etuQBPENjH4tqbBtyYE/+7f/TvyPGc6nfL+++/zox/9iJOTEye+4ivxq6urmBgEwhoQ3fE+/fRTnjx5wuXl5RsVdDA8cnCqinD3cDiMrYIg0pOmzpXtq6++4t69e5F8J4Sz/n3y5AlHR0copbi+vmY0GsVef0hmqqpiOp1ycXHBaDSKycndu3cRwgkc5XkeyXcBxv/www/RWvPll19GtCSI+wSk5OXLl/+HYXEhBA8ePODDDz/k9evXMQkIMHwwDgrwf2Dth2tirY0thBCIw8jhixcvODo6ikZKYTIgeEGMRqP4md92zCHR2J0C2bWRDm0NICYIYQvHFUYS31pSYFtfoRisluheoQO0LeJyw7Z/GRYERyFUXu/dAqYR1GuJuM2pkwIlMpdcJBJSBYnzRE/zKbIYIouMJM9QiULYHtX0HA4GvBItvW25nV+wfPmc8Xc/4I+/8wC7WfJ1renLApmXNJUlbQz7SpCbmttNw3q9Yn9S0nWW23XLfLVgef2SMlekIsGmljxxw1aq7+h1C1qDlCiVkCjpFMwwlMmAk+MTPllcc321YG/vgCJTpMoibY+0vhKxLX2zci0TIWjrNav1NYeDR6RSsG5qTN+RKId+SQHzekPbdxzev8f+85fcnJ6yf/8e5WjIerGiTFKsNfS41oSbE3dz83meUI72+eG/+Oc8++Q3nL14Rms098pHFOOCveEYa3pUqtgfzui0pW0burpFr1u++PnPuJk/x4wKysk+J+8+5L0PP+Cdowdc/epzfvpX/5l3jkfML+H69hYxznnnw/c4fXrK1dlrbpdn7A9LRJmTF2Pam4af/6f/Nzf907fzDPsHU8T/biVyZdCljxHVfwctmN6jc4R1NyQHW2TQ2nbb+3YfE4MRHvp2cUv7LwF+TZVo3Fy/kCbKg1vXyI9HvUW3Q0K+LRCce58mUAtc7Pgmkuile3HIgwgogIfsESGOeunfNPGftUOEDliEAXrX4timC1skwwmU+U81xmklRK0BvU2YrETKhCwrKMsBg8GIwWhCWQ6QSsXWowjujfH8nfvnti8SWh42JgXhykmh4t30AOabHILw8+3jQWzhsG3xEN6ze8bfshZ/a0Jwe3sbJXQHgwHHx8fcvXuXx48fv9Gn3mWZh3n2169f86tf/Yqf/vSnPH36NEr+hoMJ1sKhsgzjcFLKOJsfoP31es0vf/nLN8yAmqaJvgnT6ZQvv/wy8gSapokWv4eHh9ze3mKM4dNPP+Xx48cxmUiShOVyGUclr6+vkVJGY6W+73n69CkffPBBTBRC/z/wBYIMc/Ba+G/dAiLx8OFDqqqKWgC7ffcw+RA4C3mex2QkVOohgQjJVuA5XFxcREGn4IsQ+BBAbIP8oe2bVX44pl255oD0BDTpm22hcD5vY5Opk+U1tvfZuUQIhZICtBP4sKFSCfLGWkc9cOG/QNYvaMIKVLfBiIROOEa+kC7ICpVgRcJKJAgyhEhQInGCIWlCmiqKTDtNg65HSgtthZpfMxqWNIMhe+895vmrc3TTcvrinHZ1S7u8ZjbJmDcVhyf7HA0zzm9arlet8zq4nXP3YEaWJOi2weoe0/fYvieThk6BkXiC7cg54Fln16qbllE55Pb2lsneHlkiSZRg/2DMn/7Jh/RtQ99vULLj6P599GzE8uacF69ec/h4yezIMp3N2KyXniTl7nOZZ2SpS6Y+/N7H/OqnP2V+eeUWSiW5bWuGw5zb19doIdhUFcUgp5hOSMqCLEvBSj764Y+ZPnvC6csXfP2LW+R3vsvhR1Owmr6pEUnq+ru9pbte8Zuf/pTTxXMGR3vMRlMefPwRjx8+ZtxnPP0Pv+DvfvJfuZk/5/piTDocMJ5N2JuNUE3H5eIlF7fXtKZlrzxif3pIdb7k61/+iour37j79ZY2V/Hq2KIJyn+98U+p/35JERrGIXnY2UIF/EYhuqNlIEOFvO1RW6sJ4johcbbW0PcaIaDTfWAIEIKN9X2HQKALlar17YOtTLgNJxaDnLEGlYTWoytIlA/ULlbgTKmUaw1BWJvETlrv/yulq+iNcYiXcmtWUAwMn6eNoevdcUnCWuURTwkCiRRQpBlFMaAsSvK8JMsK0ixDytTzk0REGQwW4SF+IQXBfjJC9xEQCO2J3WkQ4doevqiy1rVVQgsh6Dxs6REitj/9u99wmnRdkv+2Z/dbE4I7d+7E0b6HDx/y8OFDTk5OIhx/eHgYe/ywdbkLaoN938de/DeDzzdH8nZPHohEuN259jChMBqNqOuao6OjGEyVUtFTYTQaxVZDMGgKKMHLly9jNhheGyYN5vM5QghevnzJzc1NVDD8+7//+9gaUUrRNA3X19eUZRmd/waDwT/oof/Bi+/Nof63/+1/i8lGgPRXq1UczQwJUCATbjYb1us1V1dXXF9fR1+BMBFirY2joruBfBf+/+/ddjkBu0TIXSfGXYJhuK9vi0OQDkpXVRjjST+u/6pSMNoior4A3glO0Hcd2vQoBIlK0Bbnz641MrQTFPQa32oAtAXTAQ1hPAwUvRB0QiKzDDuYIoT7Xggr0FXHetmxXHTIYoPpDauVdtBur6lWa1Y3c/RmA8ZQjqfcv38AxrCuO1brivV8zvLimqmEaZnRdRW9dDKlSaLIE0tPTa17siSnb2oabakblzjmScrR8R1ePvsMY3oSJclSxWQ8YDAboLuazz//Eqst09kBy95g9BV933iXUQHaMkgLrO7RnesNKyxSpKDgYO+AwWCIsZaubSmzAitSssRJ8aZZwbDIGZU5gzxHiYS+sQjTInLF8eP3KMYTLl6d8vTTT+nrDR98/8fIpAdb0607rl6d8fWTz3j27FPs3oThdMrj7/6QR3cfUp0u+ekvf8FXX/6Kq5uXPHz3gO/90z+nXfYsFi2rdcN6dYZIU1rZce/BffbHh9iF5vWTr/jqya/odOXNg97O1nYtQiQ+KQmlOl593wfkN6rRAEd7XEH6YGx2e8jbfnVg/IXvt7ZhggGPL4gt5BzEdEKV6ycBQv8aG45oOyUg/BsC+33343e68agAle9IEFvrWnnC8wuMtT7h3V4fIcIa56r6IA8cPqH3v1NSofWWZxDCMTJxoj9pSqISkiQlSVIn+pPlCBn6/8qPLSpXxQsnU+S8TnbcG30ygzVY7S+x2I134foF/MKdRDjq6PgY4qTx9xWLFeaN6+rQjK0mQ0jPtp+zTfr4A0j2tyYE//Jf/stYfU6n0wjjJ0kSR/uCdHAIQLvQcYD2w2z7N7fQl97tR4fgHxADay2LxYKzs7NYEQctg+vraw4ODtjb26MsS6qq4ujoKKIW4cSDbPLdu3dZLBbs7e1FT4AkSbi9vSVNU16+fEmSJFxeOlvXMO736tUrhsMhq9XqDbJhIEGGtsf/0S1UzyFxCuTAoGIYrIkDt2DXKyCYTj18+DC2EkKrYz6fR8QBeANx+GYiBrzxgOwSRH/fg/NNXYGw75BofZNkutvW+Mfe3jsZIrB02tBrvLwpIC1GO/K9scI5vfkmn1ECYxIwfuzQGED73q0fjZMKITRRjtWPK2rdIMwOuccaX82lqDSl3qyhr5FC0vWa89tbXlxe0pUlerPk+nKOUC2bteX65oqLVy+p5jfkgxHZyR3uH41Zrjs2bc9mveT67JTXX33N+vwVpv2AydE+SZq5hod1JjJFntKtN0ic0lvfW+q6oaobxFBysD/DWEvfNqQqJVPSJ0OKshjR1g2dsPRdy3K+YLOuSPOEZt2CFfRYirzA9O457bXG+sBjO4OtjSNjWZ/ctg1pWaCMYr1aM5oNmB1MmExGpDKhblqStEQoQZbnaNMxnO6hVMZqccPl6zO6+ud89MMfszQrlucXjjz4/2vvz54kOa40T/Snarvv7rFH5L5hBwgSVSxWVbObNd3zcFvufZj/s2VErkiJ9EhPS7M5XVXoIkCCIIAEEsjMiIzdPXzfbFW9D2Zq7pEEwVuUnsl+SIUEgIx0Nzczd9dzzne+833TLltbLa60zZ03HrLf3mX4tMvJk2/pXRzh11Ie3LrH2++8R6t1g358yTjrM1ssydSCRqfBzvYO1UoDmblcHD3j7Ogp0+klSodFB/cVrbU+sHGrM1C8KHBkIdZ73wWiVQbr68iAqU7zgGG+6wWz3Xz3S2halEG85IBJWfSpFSjTcijY7awKEJRB5dZfe5VQXCf3F4l5UQmbahvEau8r0f783FVxrTnhryBXihz9W/X4RUkmdOy8DWKQPWlZhSS5h+3Y2I6DbTlIy8aSNlJa2LZTiP8UyIJBS0QxeFwkK3kSs5oQEJDzOMr9rwzTK7LmS2tV9P+Rz1pxzbK4BwbJXCG5FE2QtXv9B4nXH19/cuwwDMMyqPf7fer1Otvb2zQajfKkDWxt+v1m9E5rXULY3ydOU35oWBHYzFhes9ksIe9ut8vl5WXZMzdtidlsVgZOYyfcaDRKsqHW+YjeeDxmPp+XssD1er1EHKSUXFxcIITg6dOnSCnLwG/Oxxx/uVyWuv/G0tkoNv5LWwZAmVCZUUdjCGWSJzMRYTwMTEI0mUxK8mWlUin/v1ar4TgOm5ubJcFwPp+zWCzKhGMdxVlvN7ycLPwpbsHLCpPmeX/sOl+VfPE7d5r5Z0ZpklTlgiEUrcCCWJxpRZplZIVEqFYuKtOkmSTVOaEryxSZ0iRaoTJBmllESUqmHYxbmygrLXJWtYYszc1n8vGfjCxeIITCcj2CdgO36qN0ymI+ZrpMmE5mbGxWWS5DxtMxg8mY+XRGxfJ5a7NFu+LyrL9kGcXMx0P6ZydcHB/Rt/NxvdvZPbZ3t3ADP6+mMoVjW7iWwBIQJhnLZUKaZdiOg3Rs4jTFcT2i5YKg3sS1rJzFLSVBvYIUkChRTKyEgCSoVIkWIQiBtBwoULYcgclQaYzUGeEipnfSJUPgOXnrxLJtbAvC2ZI4zdjeqNNsNfAcD5UqdGZUCwWOsFFpgiNt3HoD33NRacZ80ufom6+wpEs4m6LSBZ2NFlv7B1jnU5xIMnh6yvT8HCsdc+NGg+rmPs39XVrBNsOrGYPBnOliQUSKXfHxmi3ajQ5kFourBefHh/SvTkjTKYiU3Ofi1ay8hy1KYNx81oQWSJkH8Pw7thbQoayUTaBaDxb5cVeVvnECLJ9b/PsaPA0rvgEmsVDowpXQoODriKQQq33++n9FbjxWPOna9qHXCxVj2aTMX5XtgSLmFwTKvB0oS/leo5oock0QpRCWW8rjSylzEq1lY9tu3tqzCgMgaeXHEis+UT4aKYubZq7VJE+mwch1LsCavkH+U97FvCAW8g/A/NV7uXa/X8rmjOSxUtnq/Vh/yNqxyvvAn14/mBAY3wEzLmiq5s3NTXzfp9VqldnPOlRsJg0MJ+BlKeL1C3+5H21aFHt7eyVR0PM8jo+PmU6nZTVqNP3XiYnG4McEURPser0eaZry5MmTkrhoTH/SNC0tiQ050CRBZvxwY2ODarVaah9ALvBjEg/jE/AvRQkMGdOYKBlugNa58JFxMjS8CpMUGKvj6XTK5eVlSQ50Xbe0Wt7d3S21B2azWZkUrLsopmlKGIaly6K5tj9FkPy+loEZPVxHC9av81UhBPs39nNOslrb1AoYTZIrG2c6Qam8JaCyDKHzQJpmigyFThRZqokzzVIplmFKGME8TogzSZpBkiiSOCFJbFJhgc4r9JQodysTijSeIkmwPZdKs8nNB3e5eeuAVsVDLacM+jPiKMWWNaaL/DMYZyna88ELuH9zE1LNOMyYzxdM+l0GZ8eMrroIW5JqRRhFyCxj/8YuTiVAS4FQELguwnIJo5A0zRPaaq2K7biEaUy90WS+mFGtN3MROqXBsqjUqtSbTZbzEK3zzdH1A/ygynIxQyuF5TukKtddF5aNLe180woTrnpTDk9O8ao2ju/ieS5BpUa2nHPR7eNWKzTrVQLLgwyyNCvI5ykogY4VdpaPzKlMYWubg3t3mQ0HnH73HW7QQPg+7U6bg70dNrd3mE8vOfnNE7wk5eZ+lf07+zQ2OyivghYe58+7HJ2fMZwtiLIIp2rT2N6g0tym4jpkM8XZ8SHd3jGz8ApNjBSmGn01K46jnENQbux520Cy2ldzF8CXzrH42llS5rbHej04rarXsp1YNLfzLros5+zX1+o7XvSry4CYpwRKraBpE7zN2OHLLUWDAggTuNbQyXIfKrkP5u/JE3BRBPuCMCtk3uKzpP3S/rPiEvj+Sg9ArCHT+bEKgnTRWhRiJRJUhtMCach5AOtI6qpCx7xLouDzClGII5nkwPi7yGIk+g+LpZf30DXsgOtJn0nGBC+fw/pT19/DP7tlAJRVsVHMG41GvHjxgp2dHba3t6lWq1Sr1fxgRSVrAqoQgnq9TqVSKXvi65XoeoVpqkwzcWA4BOYcPO8PncayLLsmvXv79m3m83mp4W+q7kajQRiGhGFIEAQcHh6WAR4o9RKMJK/ROpjP58RxzHg8ZjKZlAhEtVotCZFSShqNBru7u997jn9qZVnG119/zSeffFLC/CYwm9aMuYeNRoNOp8O7776LELm08cXFBb1er/SHCMOQs7Mzsiyj2Wyyu7vLnTt38DyPMAw5OTlhMBiU6I35cr6cqH1fUrBe6Zsv9jqyY55r3suXWxGvYp3pB+SQvhESyb+sUthIwLXMjLPGQmGLDEcqbJlhkyB0gq0TLJ0gdJonDlqhMk2cwDJNWMQJ82XEfBExmceMlopFJAiXKVon6FSgyCWEc56Iw82bd/jg/bfZ3miRphGLRHE+GKIsm/l4wmQ8YT4ckYUxfrXOVrvNm7d26PdDwjRlNh4yuDhheHVGEi9JlwlahaAihM4FXfZv7uF4Eq1lPgbl+MSpxvHdHCZ1XBzfR5OhPcnp8wEiU4SzGePxADvYxHYc9m/fQvXGxFHuKhhFKdPBFBHYLKZTpLBz+FbnLUBHWjiWxWia8ezZKVbFQgYaaRWe9spiOrG46M+4++YNPG2h5nl7S4uUKI0QtkWGjZ0k2BmIDMg0OlO42Ny8fR9HNlikEAtwaj5aOkxP+nSfHdObHPLv/s1H/ORHP4KgziBWpJnm7Ltznp1eMMuWLPQCq2JRbTdoNzaQyiJL8nM5ff4dk1mXjBATePkz2oL/o1YcR+QDeUZxf00fX+SaAys74ZW7YVb0trUR1BGFVkCBJiilQOWCO6qoyjX57LtArcyBoEQPzHc9Fzpaq/iFWMUtWcDyYtUyEIhSCni9/VlK667xjIyy4IqpLxCy0NDI8nFJaRnIP/cRsB0H13Hzxwqz/5gJgrzat6yi2hereyGLc2W99SKKKzftCTQ6M39nAv5ahQHl65b3g/wh8tq0AeV1AWVQF0K+1FpdC+45AQDDC1k9b30ZhGWVnJQPFpTSyn8Krf3BhOA//If/gG3bbG1tlWNs4/GYs7MzxuPxtUrRMO0dxymh+GazydbWFq1WqyAgUfIM1s1vDHw/Go0QIlc/TNOUi4sLzs/POTk5KScF1pf5MqwTDkejUTk5YOB001NvtVolGXI6nfLs2TN83y/1Bj788EMWiwXdbrdMIiaTCXfu3OHOnTtkWYbruuXI5fHxMVdXV7z55ptlJf8vXe12m7fffpvnz5+X99RYRhsUo9/vc3V1de15nuexs7PDrVu3ePfdd8myjPPzc7777jtGoxGQTxw8e/aMw8NDPM8rdSTee++90ta61+sBOTKzPiVgiKDrbQQzmQErxcL1pMAgNy+vV+llcLUoiIAyKwxQNGAhhfGJX2mlo/OA5To2tnBwZB3HdnAsQSAyKmmEvZySxiHasrADl7qX0WlqLJkiRATEJGnKbBExGC24GIy4Gs8ZLRIWEcSZwFIBH777kP/lZx8R+E2++PoZv/7md0ySlLt7O/ROzxgOBoyGlyRCsNXe4M0HN6nVanx+NCAOF4y6Z3RPXzAZ93Ht/D1YLmaoKEbHGVGcMJ5OuXfvDtVKwGwxIYpHCNsizFJq1Tr1ZgvpOGRoluGSLJUsRnP6F10GvR71VpVkGbKcR1we9pCOQqUxSRwyWkzws4D+xSmtep1Mgc6yohpLmY3m/Pazr1F+wvZ2nVQlNFotql6N5WjB8eEzNjoSkc4I0wqeo/NjRyESTaPqEy0ls8kCB41r58HEs2zm05Du5JJMWHjNJlYmWIznfPn0KfNxl1ZV87/9f/6WD9//iFEkGIY2oxj6V13icMhwcslCwuZeh067Q9Wr4Vouvu0TxpLvvvqSwdURKpwiVYYqIGn5Ay5x/3cvrTNy2W0FBcycGTVLDGnXiOsYgRxzvhkyyVbxYa1SLVeiy6BYtnHRIFdJkGHmG0TYPD2PQ6ZFIQuhr7xdsRLEuU6YK69r7blmL1ltFavWBMIUiy6OQ8HZWZH8bNvO9TNkrqOxjkaveE0mSRErp8Lie78KuqDJEEKVA5A5FUKU17TOkxJWYR2tDbS/4kGAKAWEkNc/O6sJgFW6ld+KVdBHFfvVWtj7/n10fY9enXP5OyMaVbaN/kyE4N69e9RqtRJmbzQabGxscPPmzRLOj+P4mueAEeox43kPHjwo5+pPTk5IkqQc2zNVqhHKqVartFot2u02BwcH2LZNvV4vhYjWe+Dm5pi2AVCOChqSYRAEpVqfEevZ2Ngo+QgGDTD+Bb/97W85Pz/n9PSUxWJBEAS0Wi3effdddnd3SZKE5XLJZDIpeQY7Ozs8ePDgeyWL//9ZWmtu3brFv//3/57JZFImXKenpwwGgxIBWYf5DDRvEiaDJOzu7vI3f/M3OI7D73//e46Pj8vXiKKo5IE8fvyYdrvNrVu3uH37Nv1+n/Pzc8bjcfmlXOd9/LEfWPEJzOuoa1m+uPbFfBWrVakQzhaIWBH4Ln7FQ0vozyYkaUg4WSK0lUsb2zbSssikwHYcQivClj62BUrNqEcDdiZdvn38nBcLh7lbAcslERnKkXjVgFqrwfZOnVbVYX+nwt5mgyRKWC5jBrOQ/iwiaOzxv/67v2M4j/n1V1/x5dMjjvsjXCH58vkn+JUqo9mQhY6otNrcPNjmpx+8yUVvyTjWTAYDLp4+o39ywnI2QSqQ0sJ3fOI4ont5wjKcs5xNmU5mHNy6iV/JnUctIdnobFKr1vB9D8d1kJZFxZZcecd89pvf0+t1aXYa3N7fZrGcsXdnm/F8Sa/bzat8KyOJl4TLhOOvvyObWWwc7FEJ8t7sZDTn6ZNjlKU5uL1FNJsQVAIc22Uy6HN5eERry6e5v08yXrIcD/C2NnNeS5xiSYt0kRAtgThikYXEwsK1XbBhoRXCrxHIOvNexOD8lOnoDCkW3LrZ4v/9//p37B28xdkyZJZaDEczrnpDhtMrRDYncKC1s0tncwtH+ogE4lSREbI8m/PkyW9YRleodAlFjxYcjGHeq1hJEudwNcV8e/GPsdE1lb2BCExtYoiG6bW+9IpHVEL2JhEoRBCNRa+p6FfEwDzICSmKqjOvSNe/45Kco7PKOHShFFgkMX+QVxkYPGdHWFb+OSqYf3mSUsjwmuuVBcxftqSh6JOLoi0Clly1FSgeW/b7jVhZ0SKxLOMFY4iWXEMZ8oPK8u6Ze2SKcm3+LNYh+5Vtsc6MhTNr9/46KlDew5eC9vX9cx2NyM9Lmuv83seXjy7bLj+0fjAhmEwm1Go1bt++zf7+Pp1Op7Th/eKLL+h2uxwcHJQmO7VarfzQtVotOp0Oe3t71whty+WS7e1tFosFWZbR6XRwXbes0uv1Oq7rMpvNqFQqbGxs8MEHH9But/n666+5vLwsA9D6UkoxnU4xlsZhGDIcDks3xslkwt7eHmdnZyXHIEmSEvo3okDrssWmx/73f//3HB8f0+l02NjYKP0CPM/j5s2b7O7u/lnogNa5QVSv1+Orr76i38+lUV3X5d1336VarZYTFpeXl4xGo1KUaP2DZHQG+v0+33zzDY1Gg7t373Lnzh0uLi7KNsF68hTHMb1er3SSvH//Pmmacn5+Trfbvaa6aAL9+n3/3g/dH2kLvMqkoFETDLpXVIManu8SBD6QMp0lzJZjrnoXxHGKI/NxplBlJAiEI3CFTaO2wa0HP2Ljxg430oDqt6ccvLFPs7XPYDZmPh4zmcYs5jHL3pCj8xO++MYlqGyw0WqyuV1le3eTnftNHmw0aTY63Ln/iFmU8fi7Y745OudiMCBLFY6t+f3jzwiTBTgunYNbPHq0w/tvPaBRr/Pl0YAkjrk6Oeb82bdMr3oQ553tTOQsZ9uxUWnGdDQkTbIi0Alu3btNrebiOT6O5aGRZBp82yFJYlw/oLOzz9Gzr3nwYJ+aX+f4yQv2HtymWWvQCkacRxfYtoPr5xLGOsvodS8ZhA6b8ZyHNw9Qs5Tz0y79WZ+dm1v4toNVreM4LmQWcZwRqoyq4xMtY0KV5UTC0QyhFFEYYzk2nla4nsUCSbKUZFoT65RMWVhWAJOQi+4500kfLUK29wLefvQBP/+rv8RyGgyWESpMmE+mXF2NuRwMmYVjpCfYfXCPRnOTJFKkcT56IqRGTS1Onn5LvByQJUvSLC6Crg2FO90rW9pGkJvhaLEaOVwPAhKKytcI+xaeXkUgLPv50iorVxOYDNwtCyMsKU1bUBSjikWFzKowAUPwWwVWCqRQXAto+TMNxJ5TAHIuQNlyJFc+lJaR9ZUlTC6EKGfvs0KTBa2LcxMokZIZ2XHLytVCTZA0hENtkqcV4z9PKPJEQekEIz0sS2locwyZC0AZ0gQmYRDXCIRlMVQkNuZ+rXQXVsV/WthW54TAIilbO67W1+t4k6Cs0ID8WjB+EBgMpvjRau04OSG6YHj8+S2Dn//852VwPjw85PLysnQ7hLz/vbGxUTrgmQ+esfftdDosFotr43Bmbt/YFhuFQ9/3CcMQIURZbRsRHSFEiUhkWUa/3/+D6tVI/qZpWo4TGptfkywYVr6p9M34oOu6/OhHP6JSqZT9eyEEm5ubpaxxo9EoPty6ZKnats3u7i61Wu2PBsMfWoYMef/+fUajEZVKhfl8zng85vHjxwAlSvHee++hlKLX6/Hs2bOSYHmNUVxkwQbFMIjOw4cPCcOQ09NTzs7OyokQoGyLGAOoZrNJo9Hg4uKiNEZaJ/qY1/lj12PW+qbxKkmFUgp816JWCbA9h0Sr3A0vUyxGE6LFHK0yIpWS6oxQK2IBlUqToLbJwRs/4v5b7/Ngt8nOtMl80sXZbZE5HXZ8SRIuicMp0WLEctJnNBvTn6QMo5TmjSYffPQhb755l729DdqVKhYOk0XMZ9/8nhfnF0TRknbVxvYTlr1jbDkmSmMqlby989ajBzy4c4veKGQ4V8yHA86/+5bhxTHRYoIwfI1ixzBjWFoplvMJ3dMXaCAILLbeebMQSMp7rZawSaKUKEzIHIVXq7O9vc/g5JizZ6fMw5DNG9uI2KZ30SPNMiqNBpYjiKIl0XJGFEfIcMrg0uZFAlkUE8ZL2ltVXAtYxpCmxLEiFTFCSFqdLRbTJXW7hmvnY4XhYo6wLeyKT2B5TKcL/KBCliosYeWiLHYuADPr9cjCKVbV4cfvvcFb925we2+XRqVJGGYMM4twEfL020PiJGEZJqgowvFstvd3qAZVbMtFWBlpluRz3YBapFyeHJIkY7I0QhebqqnksldIKsyhe2sN1i8ChLAKo86VYuj6d00WhLv8aasq3/xdHixXhL31PrSR/c+/+2pFmDOPK8hqeXVquAamyb3WszZVenk+1orIJ2SBpusyuF7rc5s9rhBgktYaXG9eqpA0Rq7OnWx1HMsqnAXL/UmvHcP08gVFdM2/S2UrwYRZU72bazb3Ir+fL7dWyykKBELost0izb0qjp1zb/TqPV07Zvl62ugq6OLcjbMkmCao0qqQksiTQaUVVvEmrQE1+fN+QBPmBxOCOI65urpiuVyWKoAG1rdtuwz0i8WilLQ1QU4IQbVaZXNzk/F4XAoEmbHCWq3GbDYjTdNrhkNGUMgELcO2NOOLJqgbVvz3oQXG0MfA6waJMNW1UookSUrUwvS/5/M5jUaDW7dulWN8hjNQrVbRWuN5XpkQGAOlP4dMaKY2nj9/zrNnz0iShHq9XlbsrVarJAoafoSZwHj77bdLbsX6fTf8A2M7PRqNmM1mtNttqtUq+/v7bG9v8/TpU/r9fsnjMCOchkRpzqFerzOfz5lOp6U2xHpf7uX1B9DXS9f7KpbKFNKySbOMaBGi0IRpxHK+JFmGSK3zL4FlgdLILMWRFq3OLvce/QVvvPtj3ri1z0HNhV7CSf2A7lzgt3cRWpNkC4Tbpt7Zo5FOqIwuaM2XNHb3eOOtd7m5d4dapYGdOYQziygMOe93WSwSfNdjo66x0hQ3CVmGC5I7dQ77CW7ngDfeeoOHD+4SBFW+O5owjxOGF2ecH37DbHJFlkR5u0MU1Zewi80lv/YsjZlP+iiVUHEt9ra32Nzfy9XZslwyNo0UcRQzGS1wHBuUxcVln5PjQxzfYT6bMVkuuLzqInwbt+IjLAgqDaLFMhd3CpfMleQyyfB9gRfYuK6NsG2cICCdTomjsOwiC8tCSclyPqfSqKNtC5UVnxEFlpsnAJa0SLQEC4Rr49TrBLU6tYqDJTvcfXCLD996wJ3tHWzt0r9acN6/YEbG+cklp8MpaTRD2hZ+pUKzXqPhV3GEg1Cr1qywLCwFo16X6bhHmizIVFpUbbLYlFdyt69iOa6by01qA+FTRg+JSRBeHm0zQVFc4waYwLAOi+d/VXgcYEbVQAnKkT9DsDO1qOEKWJYsA6AoLcJXCrSGiGdcFi1LlhC9CZhC54ZHxpBpVcmXXRDz0is0guIv1oPy9+495l4YMqQsUYcySRJ5rz3/+sjidfLXlKvyfHWPy9c1v7s+JrgiH8pVBS8oXnOFAGhNPoFhxpXN+6hX79UKK1iNm+bvjy4TJqWNo6S+nqiVPYy1hEb/mQmBCTgmUBkd/Gq1WhLQjHiOESYSIp/JV0qVffxKpYLv+8AKVjFkviiKsCyrFAEyfALzX8iJiIPBoITUK5XKtSzYVO1m7M8EOaC0D/Y8r0Qr1h9nUIUnT56URLpWq1WSBG3bLt0ezTUbAqW5J+tjkz+01h+jlKLf7/Ob3/yGf/7nf8YoJ/q+TxAEdDodqtVqec6GQ1Gv18uJBiEEZ2dn11QJzT0TIrefPjs7o9frlZyBvb29kvdxdXV1zXBqfRLDeFast1SMp8J1Nuxqmd+tozfro4mvYqVxQhaljOcjbJ1vSInUzGc5MiAFiFTh+j629JAqwKm3uX3/R7z7o5/x1t0b7AU2LBecTzPOqNEPZxwkKfMwoTdaIn2P7a0ttvdv4jU3qTmK9z98l3Znl9kwYnA5ZjyJiOKMjJj+6JJIaVzLxfOy3EJYCKobDXxbQ5BQu/2I999/k92dbSazjLP+gvliyeWL5wwuXpBEC7TOiu96ThrKMdhVpQWQJSGz4ZLjbzWd9gaVegPb9xGZlesrJClZEhHOZ7j1XE1wslwwnE1oiFoulITFNJzTaW8jXRdHCCr1FpP+IGeZJzEJkigRVGoBvmcThxG2F6AdO5/tjHPLGqXy2Wnb9wjnC9zUxa3XQGnSKEalKZnK8IIAoUFJC+04eI06ja1N2ptb1J0DNtsuP37jAbdam6RLzcn5mOeXc84GU6QQPDu9ZJIlKJVS9z1arTqBX4NEY3kWOkvz8xG5dW0WZ1wenxFG09ysCV04X2owevav5BOcL8/10UoWQSuvDU1EN3P81yeGVkFAlQ5/+eNEkUBoBFKJos9dGPes+RBI8YfQtfmTJQutfWnssvIgaQJebjpnROoKSLyIivm+kK3B6rnMr1YrB1VdnKfpe5tLyp0H9fUECOM3opHC8AOK5KMYL1xHK6W0iwBtiIarZCbf21bcCoogXl5nkWCtkAWKxGctlJoEoqz0C0dGjFFU/jwzxipUDuVjHm6eX7RqVi0ZVR4Piu9+aVG9pg1RooYrAmqJwIgfLs5+MCGoVCrcu3ePIAgIw5Crqyum02lZPd6+fZuNjY1rFbIR1jHkQgPlm0TBtm36/X45XmiCv4F3TPZkArU5nkkeTOCP47hEIkybwgT6NE1LhT/DFTB8CFNlG1TAJAzPnj1Da43v+yUpsdFo0Gw2y+Maf4RarUatVkMpxe7ubnnd/5JlWh9ff/01n332GYvForyWOI5Ln4ZHjx5x7949qtUqy+WSXq/HyckJm5ub/Kt/9a/453/+Zw4PD5lMJmUyYK5r/bWMP0On0+G9995jZ2eHb7/9lm63y3w+L9ECc7+Pj4/LdoXxi7i8vLzmlLi+Xh43NMdbnyZ5FStdLggnY6aTGVIJfD9ABh7L5YJMKBKdECUxtu9TqTRpNjbZfPg+7/zlX/Pu3T1uVW0WownfPX3Bk2eH9CdLrAyOvn6MqFUZZRpXVjnwN7j/4C5heoM7Ww2aFZ8vHh9zcdFjtpizzDJmiabWDLgcDplNI6SAwLMJXBfP3sQOqui4SnNb8v5PPuCth7cBh8OTKdNZzKR7wsm3XxHOZvneIDVax2htIY1ojjIVYg6BSmGRJUvG/Qt+888fs7m3y6NGA8fxCOMIlaRkSUijVcV1LDZ3N2m0m8gXVk4aPDvn3v372J5FJQjQQiIcm1prE8s9w6/6JInGdgVBxSFwLESasUhmOfFL5X4P0rLRWmEJlY98qhQ8yXI5Z/NgD1TGYlZIKqsUwphlAlngUmm3aG/vsbW9S6fdYKcheedWh4Nmk3CScXQ85dnljBejkLOzAV6WMF6GpDKlsVGn3dmg6tWIo1xZ0q9I0jjM+822TRYppt0RZ8fPieMJmdJYtk3hbpNXrkZ68RUt3/UQ2IDMg6lWZYBFQ6ZWE0JarxJwpa7/GSjDsFhvMbEi/pVtEmGRqZUngYHPESIX8rEsLFuumPSsIHbHcXOhr6xwJzRqiHqdVFi0EaBICFYCZytIXpSpjRACC1mgunJFphMKISwsK0+KVzP8K5je8AjW4X0NSKXLwJ0HeVX+f05kzAm7ZdIgV2gIrLdoVt87Ic1YZ6GwWKY+L7VhDKpQBPp8rLNAEooT1HqNrIgokYCyhatNQpSjhGUyoCHL8sQhz2OKa/4Txdmf1CH4/PPPS0OharXKwcEBlmVRr9fxfZ96vU6z2SwdBi8uLqhWq6XPgLlp9XqdBw8esLGxwePHj3n+/Dn379+n0WiUTP/z83OUUjSbzWsaA2YZ8RsT6M3vjGKgCWbGEtkI+QghSrTCzOObat9U+eb1zAhlrVZje3ubIAjKloi5lizLsG2bvb09Wq3Wn7qFxRu06sGbgGn0GoBrIktGLTGOY87Ozvinf/ondnd32draKhGC/f19FosFP/vZz2g2mzx58qREUNYD8MsQ/2Aw4Fe/+hV37tzhww8/pNvt8vTpUy4uLtb6iKJMpHq9Xu5LX69z69YtptMpV1dXJElS6kqYH4NSrKMC69f9KlacpqTJAp0swfVISEmmEanOWCQJlpRsbW+jbJdg6yY33/kLHv3oJ7yx1+Z+w+LkrM/vvvyWwxeHzMZDiEK++OzXjBZjNjt73Lj5BhtBnQ0v4Ga7xeZek+VwwX/+P37DSMXU6g6Veh0RCypBg1BqNt+ooS67jHt95vM51kRRcT0qfo3JfM6bH/yIv/7Jj7GcCk+Opzy7GLGYThg8f8zV8ZekyTR3sBMSKR200LmxQiFcA3ZuRastED7CkaTphNHgiF//X/8XtUqDje09lmGI69kIkdJqtklIaW9ts7m9T63yjMV0zNnpEXfv3WZna5PAdZnHKbYb4Laq2EGdvYObXJydYzdcKlUbLAG2Q7tWYTGfEy0mOK5Hc6ODRpKlGUJpVJjRbFS5vBgQ9mdYgYtGYNsCr+IQhQna92lutNk6uEO7vUWrEbDZsnjn1ib3W02GVyG/e3rFi8GCq/GMwWDEcLpkPL9EScXW/j4bnQ0c6aIz8CpeWQXrLPeM8GyXRRwxPD+je/mUmCtsK8grYKEK5cqit/2K1DYB6rUaglzwKkeAVkx0LSiU6/Lgr7I88dEFl0RpUVT+BcEtK6YMhCxFsxAg7PVA52AXM/1aGwKeLAV98rFDM35o2imarNiPwyi+lozowqkQXbgXWrJsD5gf0+dfQfgrZMb8qDQt4qdpFedqjaKA/23HLsyLjFheXkBKK28H5AE8KdoAq3FEhMSyVFF/S5SW+ZipIWSqnJQoWfEx1hURVyvXKzCWylqr4jqLFF3KMrhTtvcMepBPVOjCx8CkQirLEYTVvVBonYDWCBzyLsHqPUDn+6+0TCJToETGEfPPTQh+/etflyQ8M+vZarV44403uH//fmkjfH5+Tr/fL7MlI1CUZVkZSIzUbxiG5QifEKIUN5pMJhwfH5ekvvl8zmg0KgOxmVAwFachppjzMgmC4zhlUDUZs5lY8H2fwWBQwvLNZpOdnR3m83lpmmSmFFzXLYV7FosFo9EI27bZ2Ngo2yD37t37F6sTGgKkkVL+xS9+gdaaL774opwEMDwL80HLsoyTk5NSS8G4UD548IBHjx7xN3/zN9y6dYt/+Id/4Ojo6NoH9GUioPnzixcvuLq64t69e7z//vvcv3+fTz/9tERrXm4JTCYTBoMBrVaLO3fuMBqN6PfzgPbyGOJ6YmF+96rMjdqtNvF4lEOtCrROybIQX8M8BU/YyEqF/Td+wq13fsK9h4+4u+2zZ1v8/sunfPLZd1z0TpHpHDUdcfz0GZPlBCUF43mf0Vef0ut1CSTc293AseF///9+zH/9zWc8enDA5ryDsF1iIVHpHLdSw/YatFoS36+Txgui2ZTZYMioN+Cd99/i3/4vP6MdNPn02yFfHl0xmFwRXj6jlTzh3bsJz08Eg7FNkubmKqkUYOU2x3mVkZsn5UWBREgH6XqoLObF8yd88g8Nbt9/RLXdJBOwu7NHmiYI6WEHAZ3dXVo7W0ymY0hSbGJqFZs0ipBYSNdH2B6WU+XkRY+g7nGws0WqbbTUCEujVYSUmlRBuJxTjWu0m7soLRlM+ghiWCjEIuLs6BlbN29QqdZIhUcSS2TQpLrTYe/GPVrVGp26y+0dj7cPOmwGNU5OFvzmSZez0YTeaMxoNGA66rMIxwhL8fD+bTobu4BHmqWkRKg0xXE8hIBatUKSpri2y3w5pntySiamSMsD28lFetIi2YU/mCP/f3qlaUI+DV8kBMYjQ+QVad41yINBnrfkwjpYKy0AUfblLZTSuLaNlC5IB11Y+korn6gAGzMC6LoeruuVMu3rbclceyQky/J2sbTyoiyJo9K1UJWCdCt0QBT7YB5scxdHI/NbIo15TZwnZGYPWYtnJkgKDZbIha/iKENaa8mLkAUUsEp2bMvOWxvFWKVt2wjbIcuSvD2QFfoqQqDMyKIQIJxSAMokP2jKP1/bN0WuPygtiS4JgS9xUco9uUhEsrI5wIpDWNIGMW8xQpUVv1GAzBO/tEgEC6Q4S1ftjyIxM0JSf2z9YEJgquV33nmHd999l1u3bgG5IM/z588JgoD79++zt7fH5uYmnU6HVqtV9sPXg4YZ0TPa/VmWUalUcByntBb2fb/8wE0mk5LsZgKMcT80SYfRPTBvhOEEVKtVgiAoEwJTib948aLkBpgevBCCjY2NEnI3sLlBO4zoUaVSoV6vlyN6t27dotlslvfqh1j0L1fJWZYxHA754osv+Prrr7l//z7vv/8+p6enPH78mLOzM6SU11wijTZAt9stv5C//e1v+eKLL7h9+zYPHjzgpz/9KbVajS+++OJa5r0eoFdEn/x+ffvtt1xcXLCzs8Nf/dVf8fjx41KsaL3aN+dxdXXFaDSi0+mws7PDcrlkMBgQhuG1JOxlaeNXtbRtkdgWTr2K1GApDZ5PSEo4jqnv3eHuT/6Wh4/e4f7+DgcNDzdO+fjTJ3z21Zd0e+eoxZQsmjEYdjkcnuK4DpmtmScz4nmfNF7ybc3l1l6bZRZyIVOCzYBeb8jx+QC/0aKzvYUvIRwlBBUX1/fxai6zsSZWM4KKy8Gth/y7v/vX7LY2+OybPk9OLrnsXzAfXWDFXTbrddr377K5MeH8csZ5b8HVJEWlFrg+Noo4CvPRrLI2EAgcpMhHbbXKePHiW7yKx/3W21i2zdXgksDLODi4hfBc9nZ32N/f5fL8kAdv3aHV7jCdRwwWS7QfYFU9LOlhuTbJYITtVHBtF1d6hPGCJI5R2mExT0iSiCzJWE67nOoBWZoSpguUzmjVHLAz3GrAPEqIRYL0XLx6ldbOHjf37+L7HrsbLo8OmtzdaBBoh2+fT/jtt6d0Rwt6wz7D8ZDJbESczKg2XG4dvEGmis1PJbgoPNdGBhZprLA9B5TCRpItIya9HoOrUzQJju0DkixLSFWSJ1dCIKSFV3llH2PCaFkqQmqlyFSGUmmJXEghikkDvSLwGV6a1hQAwqqQEgKBlaMO5Cp/lpWjArbjYTkejuMhLRv7pX22OGjZs26IdgmJ5/++Lm1sCmHz1ExlCEPm1oqsMMQqn68KvknRRljfS5I0H4fOMjMCmlfjZCkCSNO4sDTOSAsU2BKSLNWkmcK2HbI0LVQLLTJpkWYpts4KroBVmOyJVdWOzuUQshDjlZD/N3+8UH9I6jQihqr4nSpaAev3TaPziYaieNdao1Co4trJVO5mqQ1nIEdrFBlC5vdGahdt2oRGZE1oMq1KkagSsSjuuZl4+L71gwnBv/7X/5q/+Iu/YHd3l6urKz7//HNevHhBHMfcvHmTJEn47rvv6PV67O7ulr+rVqtlMrG9vV2O0s1ms9KoaHNzkziOS3VCx3FoNptMp1POzs5KxUFTsZqgYsblTJ98PbiZ6tsgCKueWv5cgz6YiYV1Lf/pdFpyG4wokRmRgZwEeffuXR49esT+/n45NvkvXVrnIkEXFxd0u13efPNNrq6uSn2En//85ywWCz7++GPOz8/LazUtDkPwM8mLUooXL14wmUy4d+8eP//5z9nb2+PTTz8tE6j1tZ64GA+E0WhU2kjv7e1hWVaZeKwnE+ZeGv6DGdk0QlP9fr80SjLnbI7xqpKCKE1obe+ShRHT0YhJv0+4mGFVXPbvv8eND/6KN998yL3dDh3XIRxO+fS7Z3zz7XOOXzwlmQ+p+zaT6YCrqx4CRRQuUIEFSYwUijCbc3R2yKefVmjV63TcgAGSw7MXuM1NLClJEAS2Q5KkREkCwsazBSKJsVE0N7f46Cfvc3Nni9PzOV+/6HHe7TIf9skWMypelaWUuFKxu9WgVpmw0Rpy0Z1w3A0ZhYULmm0DougH66LKSAo79rzPGoYLeleXNK522L55QJQtGCxDti0HmUG1scndh29j6YRHj+5z/LSHrQXTZYTX2CCoNnJ/hEaF5VygpGRwOcKvt1ksY2bTMVppkjhXfMuEQooI13NwAoemX6HZaqB1RqwshGWTkQe0Sr1Fe3eXnZ09KoHLjR2fN25ssFerQAjfno95fHzF+XDCoD9mOLpiOhsSxzMcR7HdaeM7NsLyUFmKazuQWehM5B4LMkXoHFrOMsVkPGbc67FYDpGWxLZs0jAmi0O0TvJ2jBA4UnDzRuOVfIYBsixCS43WAlRe7VuWyL0qKCYNpOmbi7KHbUyHLFtiSH6WlRdTju3kyn+ui+24BbRuY1lOofFtlf3z8p9r1bZBH1dlu2G12wX8beSNy21HgNJ2LhpUtD+UzmtarSnJsfnh1loORW9fKbUGqRfFR5aSpTESSJMYrRVpmqB0ViAKmkwZ8bSiDVT04kWhu2Aek0+gxcW1mERG5KhDMR1hW7kLolYSJVcyzmgjj76eCeUtiRz2NxhFkQzoQrhI5c9NdVYkA6B0iszAysRKYbVI6jKpQYIScd4aVPnopiQ3ZtVAgkAJXbRzTNJRZCV/bsvgzTffJIoiPvvsMw4PDzk5OWE+n+P7PhsbG7iuy3w+LxGBer1ejuoZ4R6jWWCcEJMkKXUCcl13t6zil8sl9Xqdg4ODMkAZHsK6Gp4J/Kve00oK2bgcrjtamWUCl+u6tNttNjdzdbSTkxNarVZ5LcaHwegpHBwccPfu3VKEqV6v/1l2x+uVs+u67O3t8eLFixKBUEoxHo9xXZd/+2//LY8fP+bJkyfXxiXXg+x6MjQajTg+PmZzc5O9vT3eeuutUmTIJFVwHdI3yYIZwTRTBkZy2lT+L08VmLFNo35o3sdms1maQq37VrxKhEBmYAsbaeebTJSmpMJic+8hdz/8K9546y3u77TxdErvvMvzwxc8PnyWtwYuj9jYaDKejBgMBkTzZW58lGocR9Cst1ksQmItWERLXpyf8OXXjwmVTe/ymCSL8MjIkogoDNH1BkgKMpEiCWMEGZ1Oi7v37vDmrZvM5ym/f3rJi26Xy4sLpv0rZBritAOkXyexBUQjanUH33OpBR5Vf8yz8ykXEw3CKja4nMxlRrpMcNAIdJYLF/UuL9m6eYOtg33qtQ0yISDNzVL8oMFGe4/h5RTHgv5wjrJs3EqA6/tYWrJz6w6Vuk/T8Rh2L0mXE7QlcKo2aRIRVGzq1SpYgizTWI6H7Xp4rkez1SQKQ6w4plrzUBmkWiMyi3ajTT0IuLlX552DFtuVCuEk5exyxtPzMae9MVejAcPhkPlkwHIxQsqYajXAd3LqXU6oUvl4YZb3zYWtcGyBKPTwXccnDq+YL+YoS2NZDpq8ykTlED2FWE6tanNnf+OVfY4tWbDnjc5+QUSnENSS5D1xbcR1ZD7uJwvfAtNbty27TAhy18k1hz9LlmQ6XQS1vDVhet4FsVCJ3KXQCPoWfXCzyqC4/rvivxpWY5AFgdFAGUoXuv96/dHrBY0ujJRyVMRgYFrnaElOxNMFsTBbf9baXqRKhGW1r+WBeWXUlpVBM4fzc4hDF/yAJI2v8Q9UcXyUKoSO1iSLAcvO2y9yragyvASlV5/RVKekWhlGIKmwcNMMLSWpBKE0UmlUIUklRYIqEg9RJkwWWuWaGUqmWEKDlOTCVjmSZH+PmZJZf9LtsNvtcnx8XBrvuK6L4zgsFgt2d3cx423j8bi0RjaB1QQcEwCr1SqdToeDg4NrAhUm2IdhyPb2NhsbG8xmM0ajUTkxYN4883hDYluHwMs3oOAV5G/oajZ/HT43s/paa4bDYYkMGGKimfsPgoCbN2/y9ttvs7GxQa1WK0mL/9JlpieM1sL+/j4nJydUKhVc1y3Fkoxz43vvvUe9Xuebb74p78V6oF1vmZjr+PLLL/nrv/7r0utAKVVqSaz3/14+L5NsdLvdsqVikB6TFJjHGZTAJGZJkuB5XvkTx/E1YuOfc6/+Ry2hRSne47gOtfYGdrXJvff+kjfeeZtHOy2cNOH8vMfTwxc8e/6U45NDes++peJloGtMpzNmi5BUkZOM8q9Wrl3gONhC4rgOoU743ZMvyWJNfznEdqosJwOyLCVNE6rVBnbg5xtWEpFGC4LA58bBNm/eu03F8fjiSZcnx10uLy/onhwz6Q1zTfm0QXu7Ra2Sz+0L5eLYDpuOQ9V1EFIRZQuG06LnLVhrG+T3wRCzhGUTxQmT8Yg0zdjZvYHv1ouqIyNRGUoKhBNw/vQZzWaVwWSBv71NUK1iWza27bNz8y71dh0vjphMh0hP4Fd8pPSI4iWu59NptgHBIoxB5Gp7IlWkYQrCo9VwaNYE8TJmsozQ6RLPhv3NgDcPOuxVK8xHMUfnI55djDgdTBkNJ4wGQ8bTPsvxgHg5IggklvCQWuFIgYVFlKVkInewNMptjuOipSRZpkjLJYxiwiTB8lxUZpMmCVmWgM5y3klBVNvYCNhsbb6KjzCQy/AWtDQMq10WgdvMnCOsEgq3LBspCo1/O9f9N7+3isCfawEUWvymn40GlRVRvajDy6hvEAeBznJoupTvZRX0V0sUevwCZY5fEgZNKyE/f9NZN7V//nTjr7A6tpFFluZROVOvLNCkkBjAwsD4ZgxzZcm8phxY3D9UgRxQcBh0jsJolZNPVYEgZGlWxDWTkeX7Z5zGeQLOqq+P4XsUpMAM1hIPVUwc6MIiXRWOoRlCK7xU47gaR2csM0moJShwlMbWEBT8glkOGOWtLRS2lrgKElJCmaGFBVJga4mHhWtJMv54MfuDCcEvf/nLEl5fD0CG0W9kjNftgg0j/+rqina7XQZVI/Rz+/btsjXQ7/eZzWYlcdAkDbVarfQyaDQavHjxohTNMeN0JtlYh9LXCYam554kCVLKcmwyCAKWyyVHR0ccHR2VjodHR0elmqH5cjiOw9bWFu+//z4PHz4srZjXZ1pf1kMwv/9jy9yb4+NjHMfhjTfeYLFYcHZ2BlAqIn7++efcv3+fjz76iNlsxng8LkcTDfKxLhQkhCg5BqPRiG63SxAEbG9vo7UukYLvq9Zfnkg4OzsrXR2DICjRg5eJg+ukx8ViwWQyKd9z09oxx31VSUHu4mujLahtbNK60aR94wH333qDN7ZqVLOIbw5P+eq7I07OXtC/fEH38Cnh4Iz9N+8zGg5YzJdkWqAdBylBZAmppejOR9iOTy0IqFZ8sDRPLw5J5gnSBUcsmM6nSD+gvpxSqTbZ3DsgJYV4juMKtnd2uH/3FpvNOmfnUz775oyLfp/+2Rm9k2MmgwlCuiRxTGRl7Ns71JwW89TG1ZKqZ7HRsXFdSFSXJ4dLJvOs0K8ROWSamjlohRAulhuA5RAnKfPZEt+u4cjcPjZ2LFJHol0JvssiS8imcxLXobO1TVCp5f1Tx6FZ28b1XGYXL3AaNSq+S8XzEVISpVX8Wp1KpUYWJyRqkbcx4ph4PiecTKltb7C3u4WrxsyTmNTV4C2w5Jx3b7e53Wow7i355nmfw+6Q8/GIwWTCfDBjPhkxn/SZDwdkyQwpPZKsgco0tgQ/8OmNp8U+kc9fq4JgaarTKEyYhwtineAGHunUQM55a1EW/3iBxfZuE2G1XslnGAyZrEjstBmRK1AAmesCSMsuqn675Fi5to2QFhq9hpiuQrgJnHllvS5qpBBaF0iDYC2dLKYxC3TAyoO6LEkCec+dNfEbgy6YI2Dgdci/oADCDOflP+s6AAVjkjI5URqEohRuLvrnmjXFvuIv8qevbJUtq0imLLt0hBTmBgt5rbI3Q/4Sw/Jnbf/MEwJVnH+Sxvl0h1L5WKjKENL8fV5ApanRvhAoJXPCpzajggJh2UgUjkppSkWrssRVNt04Y6AhkTYu0BYhO1KQpHCcaeaphdJQEYqOk9HEZik1Z3HGUllYKqMpMzatjKojGP25HIJqtVoKChkdgUajwebmJrdu3eLo6IiNjQ22trY4ODig3W5zdHREu90uq93t7e1SEdD3fVqtFt1uF4CdnR08z+P8/JzFYsHOzk5pQfyTn/yEVqvF8fEx//2//3e+/vprTk5OWCwWZQ/dBEfDJTDB3FTCURSVKn8msL3cbpjP59RqtdLgyIwwdjodHjx4wF/+5V/y/vvvU61W/yAB+KH1fUx/gxAMh0N+85vf8PTpU37yk5+UydLp6SlPnz4tDZX+43/8j7z11lv8+Mc/xrZtPv/8c6bT6TUBkpfNnsIw5IsvvuD999/n7//+7zk4OGB/f59Go8Hjx4/XYLGV3/bLI5GGV2C8JZrN5jWU5vuSIHNfLy8vyxaOObdXiRBkWASVAMuCaq1DZ/c2d27f5u5WgJ9F/PrzJ3z15BmDq0smwwtG3WPC6RV7BzdAWhwdPyeKwfcq1H0PQcbSsYlssJRFo15ns94kDSNOT09JsnyGXaUxmWUhMp1b+M4kg7NDKp6F6zj4VsrtO7d4+/5N9jtthv2Qjz8/5kXvinB6STo9J1r0CZVGCJf+YEZQt5lVaihfEdRqhJlFlghsC9pt+OB+iseUr1+MGS4gKdjZOWs8r3Qcy4YsZ5RnieD89IJhf0i72cxl6XwX36+hOnnfMnvyLd5ejQfeHTRVpPTAssEWOL5LNdhn2euiNITLJSrWOE6AE9TwnCpCW8gswbUdsigizVKELXCcKlvVhJ1mlUWUorIQv2VT2W5xY8fnZqPBpLvkt08ueHE1oj+aMBwNGE8HZMuI5XxCtJgSJUuEpVG2RYYoxt4ihA6pODZ24KMQhFHCbJZzhoKah0ATLhZE8RwtEiwJtlJEcYIR4dVIbAvqFYt2p8Ek+fMMzP5HLKVdtLbyKQ+ZV/qO6+B4HtK2seyVHfvK0nct+derYCvlKujmAfF6wF+Ly0BRyZayjoXKX9G9yLsqZh8xsPgaaoEJy0VoLirmvOde+CwYs6Ui+JvUYH1vUwWXQEgzQgmGQa8LIp1BOkra3hrYoIuEJE1VHvS1ICvOx/AeLGmjhVjjMZipiOL4xu2R1Z5mpJ8tY25XoBHriEt5DM1aUpHf5ywzwgQQI0hVhKfm7IuYG26ApwK6yyWHsc1U2wSW4qYfshNUcr7PeMyziUClKXsVzcMNj5YbgLb4x+MLjpYWDUdzv+Fxu17BEorM+TOVCi8vL/MH2XYpZ2tIZ//lv/yX8gN448YNoijixYsXbGxscHp6WjoemqrfjCIOBgM2NzfLKrLRaLC/v1/aHUdRxMHBQSkM9MEHH/Dw4UMuLi747rvv+OUvf8nx8XEJS5vzW1cqrNfrpZcBUCYZtVqt5DZonQsAGRVAc5xarcadO3d47733+Oijj/jxj3/8B1oDfyzAvTza93JSMJvN+Pzzz/nHf/xHOp0OWmt+9atfcXBwwGAwYGtrix/96EccHR3xy1/+kp/97Gf80z/9ExcXF7zzzju0Wi3+23/7b2WS832iP1prjo+P+elPf4plWaVokbE9/t3vfndNgGi96n95TadTkiShVquxv7/P+fn5tdbBy0iBycLXLZANV+FVJQWNVg1pVfDb2+zfvMm9g2326g7zwYz/89OvePL8W3rnp0SDLslihFQxNw92eXD/Pr/56guSNEVrhR/UqFYcFrMZsRVh+XW2alv40icZL1hMJ1iZwqr4ZNOc92L5bo5OJCnTQZdvpzOmkx5vv/02jz58m49+9Cbb9Sa93px/+vyYb0/PmY5P2Wg73Nx4B9+Bb591GS80CZrlNGR3d5Or6ZB0oQl8HxHs0AtdtrRkuy3w3rBxXMVXLyZcDmMyaeP4DmE4AzRCZVgqBqVILIdoPufy/IxoPEA5Adt729iWoFoNqFR2+Td/9xfs7W7zzbM+5/0xws43P+kEaG3nzHYdIZVCak2mlyASbK2QVEiTmHC2AJkzvTPpYFd9fKGwZZ803afqNrGCmKARcHDzJrd3b3LyYsDXzycc9id0R1dMJyOWswnRcsFiOQcb7GpAlkwJvBrgMh4uaNSWtGt15tMZllPDRiBtiSWcvEoVFsPxCEcLslhRqVVoNGqEI8E8i9FZmN8nbDJsAk/SrgfU/Ran01fyEQagVt9AWh6O7WJbVg77W0WvH1FW8kYzwLJEWQHD9SCW/1mUrDnDPzBVeamaV5DeZNlSKBIJjO4eq+SiPL5BGVZWySUjoOjvCyhgdHK0ptQoMKdU8CCKJCJPXKzVHmVRVub5gcF4J2htWhm64A4YHGFVRJl5BiPSYyYDUpGUQT9/XZ2T9cRqiqC8f2JFtAbTklslGMJwAMu9WRdJRnHnpFwlP4W1tqvBlxZV6RFYNs+TDlU1o1Wt0fJ9fKWpWxFtW/FN7NPUGbc7bdKGh600GyJBEvP7acpNz+bR7g7pHKq2wLEFpyl050veq/2ZpML1vnu73WZjY6N01MuyjP39fXzf5+nTp+zs7HDnzh16vR6LxYKtrS12d3dxXbeEyWezGfV6nfF4TBRF7O3t0Wg0WCwWDIdDKpUKw+GwHPe7e/cu7XYbIQQ7Ozu88847/OIXv+CTTz5hMplwdnbG5eUl8/mcyWSCUopKpcLt27dL3YCdnZ0y+EVRRBRFxHFcJhFGYMmcy927d3nnnXd46623uHXr1p/lU2A+eOuBejgc8vvf/55PPvmE3/3ud5yfn3P37l1+8YtfcHR0RBzHfPnll7Tbbd577z2klPzX//pf+du//Vs+/fRT/vEf/5E7d+7wd3/3d/zn//yf6fV614Kz+ZBCDuEfHx+XIky9Xo/pdMr29jY//elP+eSTT1gul3/AC3i58s+yrJz0MCTMi4uLayJKL1/ry1+UV0koBLCcAypbG9y/v8+trSpumvD06zM+ffyUi/NnzC5OmfdOGY+ukAK2tjrcvLnPZa/PbDjOCVuWhW1JlFBM0wyn0Wbn4IBld8zp1RXxIkIIie15KGVjBxVUFBJPx7iujy4k6OtNi5u393nnvbf5Nx+9S6vS4PDFmM++OeerkzPG0wt2qh4tLxeTqdcbbDbmVN2Urc0OG9sdgqDCpuXSveozmc6pVgIqwSYXM4XjQbsJb99zsKSFa004G6coJJ5TJUpGJGqJki4qDVFxigo7nDx9wq1/8zcsl4p4OiMNExKlcGsuQXsHmUo2mlWG0zmplHi+R9Co42hJy0rodLaIlyHhdICUGifw8CoBjarNYp6SSoUgQ9sW0vHxqwH7jQCZDInDHjfvHuB5DWpVD0v6fPHxN1yOGuiaS79/znQ8JAznJFlEkkSMBj2sioNtO3Tam1ipJl2EzKdjzmxBp9WkVa8QxhEZYDsWluNQb1ZZhCk1u8psMCGJUqS0cGwXlSjiJKWEpkmRQhH4VSpBjTB0WSxenf9xrdpES/taNW8KTTBtOWlifBF/1qrVss25dtCCoJdPpxQ2yvmjVtW1AMogKVkFejDWypjnlGv1/6Yfnx/7eks1nx6gvKL8mYXFcNnjBzCTAflRTDJTvsp60lHcF6Vzmew8Ufl+iFx8zz0xUw05aJCPAZr7KeVa0sG6Oq05//LurV2jkYReodKQ85o0YOyclcrbfKmQzLIKOkoZOT735CDncmQ+EgtfQ5DGKAGJtvAzC1sEaJmRCYESNrZfJdVTHHtJM2ijtc1IaDI7pdX08Zw/ntn+YEJgguatW7dK50JjmRsEAQ8ePGA0GrG/v1/OtPu+z+bmJu12u+QHmF634RpMJhPeeOON0kkxSZLS6a9erxPHMUEQlJbIlmXhum45yfD++++Xo2/Ga2E+n/PNN9+glKJarfL111+X7H0DZ3c6HbIsK/vjzWaT3d3dkji4u7tbqg9WKpVyYsHwEIBrRL719ccCX5ZljMdjzs/PGY/HTCYThsMhaZpyeHjIcrnkww8/xHEcnjx5wsXFBUmS8ODBA9I05YsvvuDu3bscHh7y5MkTsizjb//2b/nVr35Fr9e75s5ollKqVB4052VGHYUQfPTRR3z88cfl9ML6NZif9cAeRVGJYBhSpyFWmp91xMCgMy8nCq9i1W7u8ODGBi2Z0Xt2ytllj+fnZ1x0z3Cn59yuaKptF506IF0cp8JXT47o9vvE8zmVoEaCJhICaTm0tvepVasMuz0m/T5oiQhsdApJlGAjEb6N7waoTKGEAktSb7W4f/8t3nnvHf7yw7fZqLX49mjIF99d8uz0kuVkgG9pkixlNlcEgcP+wS0qlU3G0wjl2YxlSk1aOPWApmcTjicki4hIS+xgi+4ypVITtFoZD24lKK1YpBMGc43juiTaJU1CVJZvckmUcHl6hFVrkNRaVHfaCK3RaYJKIrI0wk0yhCPptAI2lg1iWcWuNnH8BhKLWTQhIR+rQoAMPKxqDSEdNBYbmx2wbSbTOUgbt9qg1mjgBxnxOECRkagFW809SCLOj4/57mTB48uAWs1CZXPieEmlEbDV2cB1HSqnVU5OXpAuElr13H1ROQlhpojiGCFtsG0c2yl1/oVWCKVo1avESYyjLWZ6Rq8bsphOC71/idY5adTzPeJ0SaYSogRmscdSO3/i0/Z/35L5cEFRcRtDnlyqCLHqlpdIv+m7G2Gea0tjJHqlzC2DDeyP1sXM/HpxYIJ53nbKirSEUi45h9aNlLDhJuRL8H3f/HUeFmt7zXrhr1SGcSLWJSZRHjaX8V1LBlR5rqJ4HwGlc6JicY7lZJp4KYi/FMDReZiXssxnyIrfmwdmWVomJznfWBTmQkVSUpyoQVG0EYMw10tug2wJgdQZiZCkwkJoic4Strhk27Y4jVLGSYxUkthShF7APLVAhQhLYiURWsdgx8SOZKJqNKSLzZw0TUiRCEtTkzF35RKR/fG9+AcTAt/32d7eLiv38XhcCtDs7u7S6/XY399nOp2WFroGXjeiQkbzf7lclnbG9+7dw/f9UuVOKUUcx+zu7paQvlElNAmFZVmlwY9h5QMsFguazSZ37tzh0aNHjMfj0pbYjPCZSYNarUa/3y/VBoMgKNsXm5ub1Gq10nXRGBYZMoghM67LC5v/fl+rwExBzGYzPvnkEz799NPSSrlSqTCZTEqVxyzLePjwITdu3CBNU4bDIU+ePOGtt97i6OiIxWJBvV4nSRIeP37M7u4uH3zwAR9//DHD4fAanyL/IuU2yeYerpP/zs/P8TyP+/fvc3R0xGw2uxbMzf+v6w6YhKrb7ZKmaTnVsX7t69oDL9+HdcTg/+n1YKdB2O3xuD9mOJ0xmk0Yjnqkw0vutH3qtsayG9iew3SZMpotOO12mcwm2GnC7e1dxkmE9hysSgXPcelfnTNZzIjSKHfwtARKgE40buYgtMhFXlyJsqHSqHNw4zZvvfM+H73/NgcbHU7P5jz+7pLnZ6eM531suaDquvi2jS3AlhLpaPzNDu7tDtX9fSxf0mnUsARkaUo0HjM8PePi6AUVx0fYLfphyk6lTqeVcStOmSwzZkdTUmVhuwEqjXPZXpmPl8VpzCJaYAcNgo09LJlXSVmakEwmDL/7hmbL52Bvh+bGJuOFZhIJZqkiFRbSCyDVpGGE61jUKxX8oEaaSeazhCSZMl+mKDfArzeoNdvUghppMqHWeEgtmOP5guUiJJzGDIaK2RRsEREtlyhSgkaVzvYmzXYLW9r4XoVGvcXTrx+znM1wLIs4TYjTjGyZsZwsqPm5ToiRudW2wnIFaZIhpIUkN2Ybj4ZMp2NUlqJUitJ5i6G9sYHrSDxfYvt1lsLFrb46HQIp8iCej+rJEpIuK+Bijy/Do9YrOoAQBXqwrpJnpv1UwQ8Qps29Ok5xjNKHYA3CN3yAHN7Xa49fPcZU9KtzW/XQjeZ+2agUgH6Zo6XQhRq3mUZ4mce0jmxmRgRtbasRRQvDbD+GdFi+TnF6WhVoQGEfbNALM5SVj/CaA4nrxy1eM9O6vN8r1CK/DysZ4UJeWueTC1qra+0DrVOkVnjE7DoOl4nDWeoT4hCQJw2xsEjSBKUnZFSJMxtLJyBDMiUImaJ0hlCSUCekAjpCsSVSMmVzHPl/9HP2gwmB0RVYtzk2I3GmSkzTlF6vh1KKra0tHMfJ9dmLqt7YGJvZ9K2tLRqNBuPxmMFggBC5z0C1Wi0dCY2ugelFCyGYTCb4vk8YhhwcHOD7Pq7rlq6GQRBwcHDAxcUFV1dXZcvCcRzCMGRvbw/Xden1emxtbZWqiK7rlm0DE7SMrPJ6FmuIci+T6EyiYPgLJokwfAkzIfD8+fPy9ZrNJmdnZwiRb0qnp6dUKhUajUbpAdHtdul0OnQ6HcbjcYmOXF5eslwuqdVqPHz4kC+++KK0J14PvuPxuBwJXOcHJEnC0dERt2/fptPplMRLc20vkw7XA38URde0HV5+/LqZ0f8sCMGs1+XstMfVeMosXDCfj1gMLpCTAZm/ia5Z1GoVbN/Dmy6I44ggcFgsMurVKlUvILUkqWOhlWI6HjKdTsgynRsKCYGSGm0DjiYlo9psUg8quc2r69DZ3uLhw4e89+4bPDjYZzpO+OrpBYenl0zGYyyVUHEcJILFMiwmWRRYLo2NTeo37+K1d0AIfJ1gaYWyFRXpo6OUq8suizCk7gVMogpNBVUvY7udcnc35nK44Gyk8iTFckmzMK+Ycuo90WxG99kFi4GFAFSaq8eF8xlXx13SpoUvNWmsicdRHlQDH29rh6Ba5yqoID2fNIpACWzLIs40caKYhxMSyyVotml2OtTrTQLbQyqLWlZFJmcMhzFTvWA2Cjk/GzPoTYkyRdAM8AKXer1GUAnwHR/P9Qm8KoH0GF916V2ek0mFcCRWmo9Tnp5d4AVVqrUmUovcrEiBkBnz5RzLcrCER5ZmRRtxSZrFaJ2r4ElLUN/aolVpInSKtgV24KEXr0Z+u1wm1hXaAHnEKSrU9UBVQNh5sa6Kyl2XQdGM86nC6hhRfBYwUwd5tqDXI+vqFEDrko2vtNEi0DlHo9xrTDuhOLeybSBMVnAdqzdBv7iwMl7rwmdhHfZf21fKQmO96NDl/EFZjef5jl5rU1BI++dqgTkyYGSfRZlUFM2Z1fUZxEnk3AuT5JiLEOuiTWXiUdAki6Qnn1vISbuaAulQ+ftokRLokE07xVOK81QjMh+kRIkMLRSOFtRIaBKilEcswNESqSSe0jSzKTVLkOl8uiQQCR0UdaWYZDDPVhyvl9cPJgSNRqMc1wvD8FpAC8OQnZ0dzs7OGAwGOI6D53lIKUubZOOSaMyEXNdla2uLi4sL+v0+y+XyWiug2+0ymUxKieB1qL7f79PpdJjNZqXGgAlUSZIwn8/LkcIvv/wSx3Fot9tlH73VapWVtml5GIEdM0lhxvLWbY4NOiGEKEl45nUNOQ9WCQHk6Mjl5SUvXrwok6Zms8l3332HMXryff/aaN7z58/Z3t4u3QXn8zlPnjzhgw8+KFs3Jmm4uLjgyy+/5Cc/+Ql7e3tkWVZW+mYZ9GWdeGiCe7fbxbIs9vb2qFarJXlwvV2glLo23riu9WAe97Kb4frv/mdZX3/zHcNFXv0t52Omgwtm/VPkcsELnXJzv0W9U8cVUBXQ8l32NlrocEKnuYHj2Hg6A52xmE0Y9rpIS+Dggc7d4LQml491BeDQ2t6l1W7jIKhWqhzcPODttx7y5v3buNLjs2enfH10Rn94hQrneBZIyyNWiuF0Rk1pEtvCrTbZaG6y0+4wni4Z9RdMkwW22aCEIg4zXC9gMZ2Quh5YVSZxhu9n1KsJBxsL7u3M6Y2WpAgs2yVJolzGNMuQZMTTKV/9t3/Gs5qkSUoaZ6RJSqJiEj1huGnj6pjp5YxoGpHoBKvts+tZBLUqTq1OtdGmezzEXUZ49Yw4VSRKs9RQabZobm7TbjZpVAKqvovn1kmGCy4PhwxHMyqeYD6ccH5yxjQckzk2O7ffxQ08XMdBxxlJGOFJr9iGJe2tTRbRgjSLsVRu0hPNl5x3L2m0Wth+BVs6+d6sNDLLicRaJfheMZNfGOqkaQw6K1rkglQLsHwskWG5EHgOvf7ylX2Os0yteva6CCWCVWAuwtw1IqEWhmuHCW3FXxTHWfXQ82ObUj4PWEroMuCVY4CGLZ8D8ygKPX/DUygpA+tjh6tADJQkxRV6sUIcVujr6tpXLQsD5V9v0WpzESZRKe+POa7EFPImgdJrr2faKyZxEawZM5vzNnbHa8FesVboFPcw95XQpcaALI/DahxSF3dB5OeW/0qCFjhaURMxbReII3xLsoXFlbBLSWNLZWw5iqa2iVJFIgUoh0yDR8Y+ETUBF5mFJSyaMqUhMpziHWi5100D19cPJgSe5/H8+fOy12yY/1prKpUKYRhyeHhYwuwmyNdqNR49esRsNivH1YwzYhiG/PrXv8bzPNrtNltbW9RqNa6urnj69ClJkpScBBNozDSAQSlGo1Gpf2B8EIwscpIkfP3110RRRLPZZGNjg06nw3K55PDwkHa7Ta/Xo1Kp4HleaTRkWgPGDwEo2xLNZrPspRs3Rc/zyiBpEhTIofPRaMRXX33F6ekpb7/9No8fP+b+/fssFgsODw8Jw5AbN25weHh4rS1hJIjv3LnDdDrl4uKC3/72t3z00Udl8nD79m2Oj4+5uLjg448/5sc//jGQ+0sY34eX4f/1EUDD5zg+Psa2bZrNJkopjo+PS1TAyCObRMAkPevB3iAkL5sZmVbKOjrwKomFg9mcKBOQpCynI8J5n0wvmKsFz64inIpLLC30csFyMiOaRzhAu1Kj0qxjWy5ZOGeymDAPZyTpEk97SEcgZI4IoHI4UFo+9eomW9VNqo0WwoatnS3eeHSf9x7co1Np8vWzPr/99pjuuIuKB6jlhDhVTPwqXquOdCRRlkvmqiQjms6ZH55y8t0xL15coOxCEAnT48yw7JTADVguFwS+xyC2qHsOVS+g3ahyb6fO46OI4SJD2DZYDkpHoFIQMUk848nn/1iY0izI4jhPGKSDW/Gx3LvE3m3Ooi6zKOTqssvy92N+NFkQvZsRRZqqFSC1JklS4liRJjDNEvyNbTb2btKpNWkENlvNgJ2NBgiHF7Mrnp05HJ5mtBoTktklo8EVQdNl7+E29z94yOxqznw6J5zNSMKI+WSK63gkOkU7Np2dPWaTAfFyiqVs1BJQKYNej1qjQbXaxLYdlBDEaYIf+LmTolZYjoXjeghpkWRJyVxPk4zjL58w3zznxs0N9lo7sEwQ2atzO4zTbE39b/UjhEBauhARNNWnCUMKuWaXTFmTmt67yCWONQUPwFS3KrcuLoJ+EbmLtkORSIj8+FrmwU8VQR5tvu+rSlmVQTU/bWlZmJHBdaJePiK7ShDyJYszyhOPTGWlNXAJI4iciphzEFSRoFP27IXIWQ9SSIQlc7OlIhGQYo0kwEpfYVXZU5yfaRGvUOOSPyElhoxYKiQW+YjBI6SQq3MsX83K72dxfy2tcRBYOMQCLE/QsSwSZbGIFfMEFplkqDMqlRpCVRjMYjLXQkvJBImjoVqrsEwSBomFsG20lRHZ4FoWTW2zwR8v2H4wIXj69GkJE5vRw1qtRrfb5eHDh3zyySel1K3v+yVxTUrJzZs3OTo6Kj0OjE7BZ599xtbWFp7nlWTFwWDA559/XkoI+77Pd999x82bNxkMBhwdHbG/v88333xDFEWlql+lUimdAI2T4Y0bN/Le4Hhckvh83+fZs2d8++23vPfee/T7fYQQ7O7ucvv2bQaDQamTcHV1xXw+L0cQDSrQaDRKJMFo+K+v9eo5DEPOzs74T//pP5EkCUEQcHp6yo0bN0iShN/+9rdAnnAZ50LDOTg7O6PX6/HRRx9xenrKfD7nd7/7HXfv3qXT6TCdTmm1WvR6PS4uLvjiiy949OgRtm3z1Vdf/cE44Lo5kjlP8/8nJyc8evSIjY2NMtFaXyZRMdf1Mvy/Huhf5jC8bGr0qloGic6YTUdE85AsnmG7Dr7fod3eJAlT3FaLb54fEi1mzKdTxpMJiRRIYfPujXv0rq64mg5JrAy76iKtKp5ysFyPUC0KuVeQmcDFIfDqKGUhtEut1eCNRw94/4277DaanJ7N+O+fH3I+uEKEY7YbHkvpMpgtiESGUhm1ZjMn9iUJ4WzKs96Ez6+GfPfV7zi5eM69dz6ksrmFEMUGZUGl7nJ3f5fL0zHKCwhllbmGJimBX2OjtWB/UzJ8kROYDKSvdIZjOWTaBQecBjiZTTwOicMY4Vl4gWTSm7PZ2OYke044HJBGc0JC/uGfPuahVSXzBLPTI4STgZR0p0syy8Fvt9k9uEsjqFGz4dZmje12HR0KDk9HvDgfUN/awumPiOw5omPRbm2ys7PJz376c5JUIJs2aMUiXBKrkCRNkTIniFnCphpUsFGEts1UjIntKZ7vMlvOGQ2HeWJLBVGI9uQyv5o0mZNlMZ7v4PuVPIiKAmzXmkTNcy8E6eE7HWZZjPoz5Mr/Ry2VZUXQNeS4FQSvs3x0bTWTv6YxIlS5/ZdhWuTsdp1DJyWqkK11REzwy+2Ni4CHRoscAlBZYfhTeD1QKgQaxEyUkLkJ/HJtpE8X5ABlHLuLREUKq2Tkw6ptIYvgapV7T7bKGQqbYlO/K52hM7M/FUgKEkWGTstLzM9nDeGXReslx5/yXxe+jrkyqaA8bzDJh6agM+aJgWnDAJYwn5ec1CqFBCkROlclVKa4klautqgztHaY4rBYpjjhBGRK4kAsNCkOsfCZZxbOVCCkTWo7CCQZikEGk0zjxKDxSbHQUjGXLqkWXEUZKlPYafBHP2fiVY+FvV6v1+v1er1er9fr9erXq8PAXq/X6/V6vV6v1+v1+p9mvU4IXq/X6/V6vV6v1+v1ep0QvF6v1+v1er1er9fr9ToheL1er9fr9Xq9Xq/Xi9cJwev1er1er9fr9Xq9XrxOCF6v1+v1er1er9fr9QL+f64suEjRelz8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 648x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dls.show_batch(nrows=1, ncols=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember that if anything goes wrong when you create your `DataLoaders` from your `DataBlock`, or if you want to view exactly what happens with your `DataBlock`, you can use the `summary` method we presented in the last chapter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our data is now ready for training a model. As we will see, nothing is going to change when we create our `Learner`, but behind the scenes, the fastai library will pick a new loss function for us: binary cross-entropy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Binary Cross-Entropy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we'll create our `Learner`. We saw in <<chapter_mnist_basics>> that a `Learner` object contains four main things: the model, a `DataLoaders` object, an `Optimizer`, and the loss function to use. We already have our `DataLoaders`, we can leverage fastai's `resnet` models (which we'll learn how to create from scratch later), and we know how to create an `SGD` optimizer. So let's focus on ensuring we have a suitable loss function. To do this, let's use `cnn_learner` to create a `Learner`, so we can look at its activations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = cnn_learner(dls, resnet18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also saw that the model in a `Learner` is generally an object of a class inheriting from `nn.Module`, and that we can call it using parentheses and it will return the activations of a model. You should pass it your independent variable, as a mini-batch. We can try it out by grabbing a mini batch from our `DataLoader` and then passing it to the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([64, 20])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x,y = to_cpu(dls.train.one_batch())\n",
    "activs = learn.model(x)\n",
    "activs.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Think about why `activs` has this shape—we have a batch size of 64, and we need to calculate the probability of each of 20 categories. Here’s what one of those activations looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([ 0.7476, -1.1988,  4.5421, -1.5915, -0.6749,  0.0343, -2.4930, -0.8330, -0.3817, -1.4876, -0.1683,  2.1547, -3.4151, -1.1743,  0.1530, -1.6801, -2.3067,  0.7063, -1.3358, -0.3715],\n",
       "       grad_fn=<SelectBackward>)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "activs[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> note: Getting Model Activations: Knowing how to manually get a mini-batch and pass it into a model, and look at the activations and loss, is really important for debugging your model. It is also very helpful for learning, so that you can see exactly what is going on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "They aren’t yet scaled to between 0 and 1, but we learned how to do that in <<chapter_mnist_basics>>, using the `sigmoid` function. We also saw how to calculate a loss based on this—this is our loss function from <<chapter_mnist_basics>>, with the addition of `log` as discussed in the last chapter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def binary_cross_entropy(inputs, targets):\n",
    "    inputs = inputs.sigmoid()\n",
    "    return -torch.where(targets==1, inputs, 1-inputs).log().mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that because we have a one-hot-encoded dependent variable, we can't directly use `nll_loss` or `softmax` (and therefore we can't use `cross_entropy`):\n",
    "\n",
    "- `softmax`, as we saw, requires that all predictions sum to 1, and tends to push one activation to be much larger than the others (due to the use of `exp`); however, we may well have multiple objects that we're confident appear in an image, so restricting the maximum sum of activations to 1 is not a good idea. By the same reasoning, we may want the sum to be *less* than 1, if we don't think *any* of the categories appear in an image.\n",
    "- `nll_loss`, as we saw, returns the value of just one activation: the single activation corresponding with the single label for an item. This doesn't make sense when we have multiple labels.\n",
    "\n",
    "On the other hand, the `binary_cross_entropy` function, which is just `mnist_loss` along with `log`, provides just what we need, thanks to the magic of PyTorch's elementwise operations. Each activation will be compared to each target for each column, so we don't have to do anything to make this function work for multiple columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> j: One of the things I really like about working with libraries like PyTorch, with broadcasting and elementwise operations, is that quite frequently I find I can write code that works equally well for a single item or a batch of items, without changes. `binary_cross_entropy` is a great example of this. By using these operations, we don't have to write loops ourselves, and can rely on PyTorch to do the looping we need as appropriate for the rank of the tensors we're working with."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PyTorch already provides this function for us. In fact, it provides a number of versions, with rather confusing names!\n",
    "\n",
    "`F.binary_cross_entropy` and its module equivalent `nn.BCELoss` calculate cross-entropy on a one-hot-encoded target, but do not include the initial `sigmoid`. Normally for one-hot-encoded targets you'll want `F.binary_cross_entropy_with_logits` (or `nn.BCEWithLogitsLoss`), which do both sigmoid and binary cross-entropy in a single function, as in the preceding example.\n",
    "\n",
    "The equivalent for single-label datasets (like MNIST or the Pet dataset), where the target is encoded as a single integer, is `F.nll_loss` or `nn.NLLLoss` for the version without the initial softmax, and `F.cross_entropy` or `nn.CrossEntropyLoss` for the version with the initial softmax.\n",
    "\n",
    "Since we have a one-hot-encoded target, we will use `BCEWithLogitsLoss`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(1.0342, grad_fn=<BinaryCrossEntropyWithLogitsBackward>)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss_func = nn.BCEWithLogitsLoss()\n",
    "loss = loss_func(activs, y)\n",
    "loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We don't actually need to tell fastai to use this loss function (although we can if we want) since it will be automatically chosen for us. fastai knows that the `DataLoaders` has multiple category labels, so it will use `nn.BCEWithLogitsLoss` by default.\n",
    "\n",
    "One change compared to the last chapter is the metric we use: because this is a multilabel problem, we can't use the accuracy function. Why is that? Well, accuracy was comparing our outputs to our targets like so:\n",
    "\n",
    "```python\n",
    "def accuracy(inp, targ, axis=-1):\n",
    "    \"Compute accuracy with `targ` when `pred` is bs * n_classes\"\n",
    "    pred = inp.argmax(dim=axis)\n",
    "    return (pred == targ).float().mean()\n",
    "```\n",
    "\n",
    "The class predicted was the one with the highest activation (this is what `argmax` does). Here it doesn't work because we could have more than one prediction on a single image. After applying the sigmoid to our activations (to make them between 0 and 1), we need to decide which ones are 0s and which ones are 1s by picking a *threshold*. Each value above the threshold will be considered as a 1, and each value lower than the threshold will be considered a 0:\n",
    "\n",
    "```python\n",
    "def accuracy_multi(inp, targ, thresh=0.5, sigmoid=True):\n",
    "    \"Compute accuracy when `inp` and `targ` are the same size.\"\n",
    "    if sigmoid: inp = inp.sigmoid()\n",
    "    return ((inp>thresh)==targ.bool()).float().mean()\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we pass `accuracy_multi` directly as a metric, it will use the default value for `threshold`, which is 0.5. We might want to adjust that default and create a new version of `accuracy_multi` that has a different default. To help with this, there is a function in Python called `partial`. It allows us to *bind* a function with some arguments or keyword arguments, making a new version of that function that, whenever it is called, always includes those arguments. For instance, here is a simple function taking two arguments:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Hello Jeremy.', 'Ahoy! Jeremy.')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def say_hello(name, say_what=\"Hello\"): return f\"{say_what} {name}.\"\n",
    "say_hello('Jeremy'),say_hello('Jeremy', 'Ahoy!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can switch to a French version of that function by using `partial`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Bonjour Jeremy.', 'Bonjour Sylvain.')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = partial(say_hello, say_what=\"Bonjour\")\n",
    "f(\"Jeremy\"),f(\"Sylvain\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now train our model. Let's try setting the accuracy threshold to 0.2 for our metric:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy_multi</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.942663</td>\n",
       "      <td>0.703737</td>\n",
       "      <td>0.233307</td>\n",
       "      <td>00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.823155</td>\n",
       "      <td>0.555462</td>\n",
       "      <td>0.298347</td>\n",
       "      <td>00:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.606124</td>\n",
       "      <td>0.202830</td>\n",
       "      <td>0.815060</td>\n",
       "      <td>00:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.360787</td>\n",
       "      <td>0.123490</td>\n",
       "      <td>0.942052</td>\n",
       "      <td>00:06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy_multi</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.134407</td>\n",
       "      <td>0.118581</td>\n",
       "      <td>0.949661</td>\n",
       "      <td>00:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.117051</td>\n",
       "      <td>0.104169</td>\n",
       "      <td>0.950657</td>\n",
       "      <td>00:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.097517</td>\n",
       "      <td>0.101461</td>\n",
       "      <td>0.952789</td>\n",
       "      <td>00:08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = cnn_learner(dls, resnet50, metrics=partial(accuracy_multi, thresh=0.2))\n",
    "learn.fine_tune(3, base_lr=3e-3, freeze_epochs=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Picking a threshold is important. If you pick a threshold that's too low, you'll often be failing to select correctly labeled objects. We can see this by changing our metric, and then calling `validate`, which returns the validation loss and metrics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(#2) [0.10146083682775497,0.9298606514930725]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.metrics = partial(accuracy_multi, thresh=0.1)\n",
    "learn.validate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you pick a threshold that's too high, you'll only be selecting the objects for which your model is very confident:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(#2) [0.10146083682775497,0.943486213684082]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.metrics = partial(accuracy_multi, thresh=0.99)\n",
    "learn.validate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can find the best threshold by trying a few levels and seeing what works best. This is much faster if we just grab the predictions once:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds,targs = learn.get_preds()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we can call the metric directly. Note that by default `get_preds` applies the output activation function (sigmoid, in this case) for us, so we'll need to tell `accuracy_multi` to not apply it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.9575)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_multi(preds, targs, thresh=0.9, sigmoid=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now use this approach to find the best threshold level:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs = torch.linspace(0.05,0.95,29)\n",
    "accs = [accuracy_multi(preds, targs, thresh=i, sigmoid=False) for i in xs]\n",
    "plt.plot(xs,accs);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, we're using the validation set to pick a hyperparameter (the threshold), which is the purpose of the validation set. Sometimes students have expressed their concern that we might be *overfitting* to the validation set, since we're trying lots of values to see which is the best. However, as you see in the plot, changing the threshold in this case results in a smooth curve, so we're clearly not picking some inappropriate outlier. This is a good example of where you have to be careful of the difference between theory (don't try lots of hyperparameter values or you might overfit the validation set) versus practice (if the relationship is smooth, then it's fine to do this).\n",
    "\n",
    "This concludes the part of this chapter dedicated to multi-label classification. Next, we'll take a look at a regression problem."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's easy to think of deep learning models as being classified into domains, like *computer vision*, *NLP*, and so forth. And indeed, that's how fastai classifies its applications—largely because that's how most people are used to thinking of things.\n",
    "\n",
    "But really, that's hiding a more interesting and deeper perspective. A model is defined by its independent and dependent variables, along with its loss function. That means that there's really a far wider array of models than just the simple domain-based split. Perhaps we have an independent variable that's an image, and a dependent that's text (e.g., generating a caption from an image); or perhaps we have an independent variable that's text and dependent that's an image (e.g., generating an image from a caption—which is actually possible for deep learning to do!); or perhaps we've got images, texts, and tabular data as independent variables, and we're trying to predict product purchases... the possibilities really are endless.\n",
    "\n",
    "To be able to move beyond fixed applications, to crafting your own novel solutions to novel problems, it helps to really understand the data block API (and maybe also the mid-tier API, which we'll see later in the book). As an example, let's consider the problem of *image regression*. This refers to learning from a dataset where the independent variable is an image, and the dependent variable is one or more floats. Often we see people treat image regression as a whole separate application—but as you'll see here, we can treat it as just another CNN on top of the data block API.\n",
    "\n",
    "We're going to jump straight to a somewhat tricky variant of image regression, because we know you're ready for it! We're going to do a key point model. A *key point* refers to a specific location represented in an image—in this case, we'll use images of people and we'll be looking for the center of the person's face in each image. That means we'll actually be predicting *two* values for each image: the row and column of the face center. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assemble the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the [Biwi Kinect Head Pose dataset](https://icu.ee.ethz.ch/research/datsets.html) for this section. We'll begin by downloading the dataset as usual:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = untar_data(URLs.BIWI_HEAD_POSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hide\n",
    "Path.BASE_PATH = path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see what we've got!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(#50) [Path('01'),Path('01.obj'),Path('02'),Path('02.obj'),Path('03'),Path('03.obj'),Path('04'),Path('04.obj'),Path('05'),Path('05.obj')...]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path.ls().sorted()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 24 directories numbered from 01 to 24 (they correspond to the different people photographed), and a corresponding *.obj* file for each (we won't need them here). Let's take a look inside one of these directories:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(#1000) [Path('01/depth.cal'),Path('01/frame_00003_pose.txt'),Path('01/frame_00003_rgb.jpg'),Path('01/frame_00004_pose.txt'),Path('01/frame_00004_rgb.jpg'),Path('01/frame_00005_pose.txt'),Path('01/frame_00005_rgb.jpg'),Path('01/frame_00006_pose.txt'),Path('01/frame_00006_rgb.jpg'),Path('01/frame_00007_pose.txt')...]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(path/'01').ls().sorted()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inside the subdirectories, we have different frames, each of them come with an image (*\\_rgb.jpg*) and a pose file (*\\_pose.txt*). We can easily get all the image files recursively with `get_image_files`, then write a function that converts an image filename to its associated pose file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Path('13/frame_00349_pose.txt')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_files = get_image_files(path)\n",
    "def img2pose(x): return Path(f'{str(x)[:-7]}pose.txt')\n",
    "img2pose(img_files[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at our first image:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(480, 640)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im = PILImage.create(img_files[0])\n",
    "im.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=160x120 at 0x7F51A2492390>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im.to_thumb(160)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Biwi dataset website used to explain the format of the pose text file associated with each image, which shows the location of the center of the head. The details of this aren't important for our purposes, so we'll just show the function we use to extract the head center point:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "cal = np.genfromtxt(path/'01'/'rgb.cal', skip_footer=6)\n",
    "def get_ctr(f):\n",
    "    ctr = np.genfromtxt(img2pose(f), skip_header=3)\n",
    "    c1 = ctr[0] * cal[0][0]/ctr[2] + cal[0][2]\n",
    "    c2 = ctr[1] * cal[1][1]/ctr[2] + cal[1][2]\n",
    "    return tensor([c1,c2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function returns the coordinates as a tensor of two items:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([384.6370, 259.4787])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_ctr(img_files[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can pass this function to `DataBlock` as `get_y`, since it is responsible for labeling each item. We'll resize the images to half their input size, just to speed up training a bit.\n",
    "\n",
    "One important point to note is that we should not just use a random splitter. The reason for this is that the same people appears in multiple images in this dataset, but we want to ensure that our model can generalize to people that it hasn't seen yet. Each folder in the dataset contains the images for one person. Therefore, we can create a splitter function that returns true for just one person, resulting in a validation set containing just that person's images.\n",
    "\n",
    "The only other difference tfrom the previous data block examples is that the second block is a `PointBlock`. This is necessary so that fastai knows that the labels represent coordinates; that way, it knows that when doing data augmentation, it should do the same augmentation to these coordinates as it does to the images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "biwi = DataBlock(\n",
    "    blocks=(ImageBlock, PointBlock),\n",
    "    get_items=get_image_files,\n",
    "    get_y=get_ctr,\n",
    "    splitter=FuncSplitter(lambda o: o.parent.name=='13'),\n",
    "    batch_tfms=[*aug_transforms(size=(240,320)), \n",
    "                Normalize.from_stats(*imagenet_stats)]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> important: Points and Data Augmentation: We're not aware of other libraries (except for fastai) that automatically and correctly apply data augmentation to coordinates. So, if you're working with another library, you may need to disable data augmentation for these kinds of problems."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before doing any modeling, we should look at our data to confirm it seems okay:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dls = biwi.dataloaders(path)\n",
    "dls.show_batch(max_n=9, figsize=(8,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's looking good! As well as looking at the batch visually, it's a good idea to also look at the underlying tensors (especially as a student; it will help clarify your understanding of what your model is really seeing):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([64, 3, 240, 320]), torch.Size([64, 1, 2]))"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xb,yb = dls.one_batch()\n",
    "xb.shape,yb.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make sure that you understand *why* these are the shapes for our mini-batches."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's an example of one row from the dependent variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-0.3375,  0.2193]], device='cuda:5')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yb[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, we haven't had to use a separate *image regression* application; all we've had to do is label the data, and tell fastai what kinds of data the independent and dependent variables represent."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's the same for creating our `Learner`. We will use the same function as before, with one new parameter, and we will be ready to train our model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training a Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As usual, we can use `cnn_learner` to create our `Learner`. Remember way back in <<chapter_intro>> how we used `y_range` to tell fastai the range of our targets? We'll do the same here (coordinates in fastai and PyTorch are always rescaled between -1 and +1):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = cnn_learner(dls, resnet18, y_range=(-1,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`y_range` is implemented in fastai using `sigmoid_range`, which is defined as:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid_range(x, lo, hi): return torch.sigmoid(x) * (hi-lo) + lo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is set as the final layer of the model, if `y_range` is defined. Take a moment to think about what this function does, and why it forces the model to output activations in the range `(lo,hi)`.\n",
    "\n",
    "Here's what it looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_function(partial(sigmoid_range,lo=-1,hi=1), min=-4, max=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We didn't specify a loss function, which means we're getting whatever fastai chooses as the default. Let's see what it picked for us:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FlattenedLoss of MSELoss()"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dls.loss_func"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This makes sense, since when coordinates are used as the dependent variable, most of the time we're likely to be trying to predict something as close as possible; that's basically what `MSELoss` (mean squared error loss) does. If you want to use a different loss function, you can pass it to `cnn_learner` using the `loss_func` parameter.\n",
    "\n",
    "Note also that we didn't specify any metrics. That's because the MSE is already a useful metric for this task (although it's probably more interpretable after we take the square root). \n",
    "\n",
    "We can pick a good learning rate with the learning rate finder:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "SuggestedLRs(lr_min=0.005754399299621582, lr_steep=0.033113110810518265)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.lr_find()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll try an LR of 2e-2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.050496</td>\n",
       "      <td>0.008238</td>\n",
       "      <td>00:36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.007744</td>\n",
       "      <td>0.004763</td>\n",
       "      <td>00:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.003334</td>\n",
       "      <td>0.000388</td>\n",
       "      <td>00:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.001468</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>00:48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr = 1e-2\n",
    "learn.fine_tune(3, lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generally when we run this we get a loss of around 0.0001, which corresponds to an average coordinate prediction error of:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.01"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.sqrt(0.0001)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This sounds very accurate! But it's important to take a look at our results with `Learner.show_results`. The left side are the actual (*ground truth*) coordinates and the right side are our model's predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.show_results(ds_idx=1, nrows=3, figsize=(6,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's quite amazing that with just a few minutes of computation we've created such an accurate key points model, and without any special domain-specific application. This is the power of building on flexible APIs, and using transfer learning! It's particularly striking that we've been able to use transfer learning so effectively even between totally different tasks; our pretrained model was trained to do image classification, and we fine-tuned for image regression."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In problems that are at first glance completely different (single-label classification, multi-label classification, and regression), we end up using the same model with just different numbers of outputs. The loss function is the one thing that changes, which is why it's important to double-check that you are using the right loss function for your problem.\n",
    "\n",
    "fastai will automatically try to pick the right one from the data you built, but if you are using pure PyTorch to build your `DataLoader`s, make sure you think hard when you have to decide on your about your choice of loss function, and remember that you most probably want:\n",
    "\n",
    "- `nn.CrossEntropyLoss` for single-label classification\n",
    "- `nn.BCEWithLogitsLoss` for multi-label classification\n",
    "- `nn.MSELoss` for regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Questionnaire"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. How could multi-label classification improve the usability of the bear classifier?\n",
    "1. How do we encode the dependent variable in a multi-label classification problem?\n",
    "1. How do you access the rows and columns of a DataFrame as if it was a matrix?\n",
    "1. How do you get a column by name from a DataFrame?\n",
    "1. What is the difference between a `Dataset` and `DataLoader`?\n",
    "1. What does a `Datasets` object normally contain?\n",
    "1. What does a `DataLoaders` object normally contain?\n",
    "1. What does `lambda` do in Python?\n",
    "1. What are the methods to customize how the independent and dependent variables are created with the data block API?\n",
    "1. Why is softmax not an appropriate output activation function when using a one hot encoded target?\n",
    "1. Why is `nll_loss` not an appropriate loss function when using a one-hot-encoded target?\n",
    "1. What is the difference between `nn.BCELoss` and `nn.BCEWithLogitsLoss`?\n",
    "1. Why can't we use regular accuracy in a multi-label problem?\n",
    "1. When is it okay to tune a hyperparameter on the validation set?\n",
    "1. How is `y_range` implemented in fastai? (See if you can implement it yourself and test it without peeking!)\n",
    "1. What is a regression problem? What loss function should you use for such a problem?\n",
    "1. What do you need to do to make sure the fastai library applies the same data augmentation to your inputs images and your target point coordinates?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Further Research"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Read a tutorial about Pandas DataFrames and experiment with a few methods that look interesting to you. See the book's website for recommended tutorials.\n",
    "1. Retrain the bear classifier using multi-label classification. See if you can make it work effectively with images that don't contain any bears, including showing that information in the web application. Try an image with two different kinds of bears. Check whether the accuracy on the single-label dataset is impacted using multi-label classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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